14 October, 2025

By Kelsie Nabben

Coordination under Uncertainty

As the digital world becomes ever more entangled with global finance, governance, and everyday life, the question of how to coordinate securely under conditions of uncertainty has never been more pressing.

My forthcoming book, Decentralised Digital Security: Code, Community, Crisis, explores this topic through a deep ethnographic dive into blockchain ecosystems-with a focus on security not only as a technical property, but also a social practice.

The book draws on years of ethnographic research conducted as major incidents unfolded across blockchain ecosystems-often in real time. It follows the engineers, white-hat hackers, and other community responders who collectively respond, rescue, pause, repair, postmortem and prepare for the next crises (notably, the not-for-profit Security Alliance, or SEAL). For the first time, it documents how security occurs within blockchain ecosystems, revealing how decentralised systems depend on emergent forms of coordination, including institutions, standards, reputation, and moral codes.

This blog post shares eight critical insights that frame the book.

1. Blockchain is framed by a persistent state of insecurity

In decentralised systems, insecurity is not an exception, it is the norm. The open, permissionless nature of public blockchains and the financial value at stake means that vulnerability and threats are constant. This persistent insecurity shapes how actors organise, communicate, and innovate. Rather than aiming to eliminate insecurity or ‘achieve’ security, these communities learn to live with it: treating threats as a shared coordination problem that must be constantly managed.

2. Insecurity is the greatest threat to the legitimacy of public blockchain ecosystems

For public blockchain systems that claim to offer trust without intermediaries, insecurity undermines not only user confidence but broader legitimacy of the industry. Each exploit or governance failure threatens the foundational claim of public blockchains: that code can be law (i.e. algorithmic governance). The book shows how the legitimacy of blockchain ecosystems depends on the ability to respond collectively to crises and improve user outcomes-turning moments of breakdown into demonstrations of precarity, innovation, and resilience.

3. Security is social: from technical vulnerabilities to social engineering

While smart contract bugs and hacks often make headlines, the book shows that security is a social coordination phenomenon. Firstly, social engineering often creates the possibility for phishing attacks and major exploits. Secondly, crisis response is inherently reputational, often informal, and socially coordinated-both within blockchain ecosystems, and with external authorities (i.e. Federal law enforcement agencies). Blockchain security, therefore, must be understood as a sociotechnical achievement-requiring not only auditing and formal verification, but rules of engagement, communication practices, and shared moral economies of response and repair.

4. The rise of the blockchain white hat hacker

The book identifies that a new character has emerged in the security landscape: the blockchain white-hat hacker. White hats are ‘for good’ hackers-highly proficient in their technical abilities and ideologically motivated to help the blockchain industry fulfill its potential. These actors often coordinate ‘war room’ crisis responses and intervene in real time (publicly, on chain) to mitigate or reverse hacks. They are responsible for the recovery of millions in stolen assets and design of numerous opensource software code security solutions. They state their motivations as altruistic, and their actions are bound by their own codes of conduct. The blockchain white hat hacker embodies cypherpunk ideals and the moral frontier of decentralised security. The book examines the rise of these “ethical exploiters” and the delicate reputation and legitimacy claims that underpin their work. It also traces the novel initiatives that are paving the way to protect white hat rights and allow users and protocols to consent to their response efforts (such as SEAL Safe Harbour and SEAL911).

5. Beyond hacks: Physical and geopolitical threats

Blockchain infrastructures are not insulated from the physical and political world. From gang kidnappings to state-sponsored exploits by sanctioned regimes, the geopolitics of security are increasingly intertwined with decentralised digital systems. The Lazarus Group and other Advanced Persistent Threats illustrate how cyber operations blur the boundaries between financial crime, warfare, and governance. Security in blockchain networks must therefore be seen as a geopolitical concern that requires coordination with traditional nation-state authorities.

6. Emergent security coordination: the rise of collective initiatives like SEAL

In the face of persistent threats, decentralised communities have created innovative security coordination initiatives. A key example that the book traces is the Security Alliance (SEAL), a cross-protocol coalition that mobilises incident response across ecosystems. These initiatives exemplify a new form of collective intelligence and diplomacy that operates across institutional boundaries to trace, freeze, and recover assets. They represent some of the most promising frontiers in decentralised digital security.

7. The need for incentive alignment

Decentralised security relies on incentives-from donations to bug bounties. Yet, economic incentives must be reliably organised to sustain security practices. Furthermore, effective defence requires incentive systems that combine economic reward with moral purpose, a sense of civic duty, and institutional accountability. Security is strongest where incentives are aligned with ethics.

8. Security for users: Towards a culture of shared responsibility

User security in blockchain ecosystems remains one of the most under-examined challenges of all. From managing private keys to recognising scams, individuals using blockchain infrastructure are required to bear the weight of ‘self-sovereignty.’ The book details the experiences of user losses (myself included), as well as the buffers (i.e. SEAL911 emergency response help line, and protocol or exchange pay outs). It argues that genuine user security demands designing for insecurity and shared responsibility to build infrastructures that empower users through better defaults, interfaces, and education.

In Closing

Decentralised Digital Security: Code, Community, Crisis tells the story of how people coordinate under pressure when the stakes are global, the code is public, and the rules are still being written.

It is also a call to reimagine security as a collective endeavour-one that spans codebases, communities, and crises. Through ethnographic insight and analytical depth, the book situates blockchain not at the periphery of cybersecurity, but at its evolving core.

Note: The book is anticipated for release in early 2026. It will be published Open Access, with thanks to a generous grant from the Ethereum Foundation. Thank you also to each research participant, notably the Security Alliance, as well colleagues for feedback, as detailed in the acknowledgements of the book.

Originally published at https://kelsienabben.substack.com.

8 Critical Insights from My Forthcoming Book on Blockchain Security was originally published in BlockScience on Medium, where people are continuing the conversation by highlighting and responding to this story.

In his EPIC 2025 keynote discussion, Peter Sarlin of Silo AI pointed out that generative AI is best framed as the first massively successful consumer-facing AI product. Part of its innovation is an interface that makes it easy to query and elicit responses. Products like ChatGPT have been so successful that we risk mistaking such products for the full scope of what AI is or might be.

As our EPIC paper argues, a problem with these products is that they strip away key processes that shape knowledge and determine how it is taken up in the world, including shared rules and group practices. Organisations are devices through which we enact this kind of social ordering. They coordinate their members’ actions through internal institutional structures.

We are asking: What if AI doesn’t erase organisational rules, practices and beliefs, but is informed and bound by them?

This is the horizon of what we are calling Artificial Organisational Intelligence (AOI).

What AOI Is (and Isn’t)

AOI is not about AI doing the job of organisations. It’s not about replacing firms, universities, or communities with chatbots.

Instead, it’s about making organisation-level knowledge machine-readable so that it may be accessible and legible to humans through AI. What if you could talk directly to an organisation rather than just a representative of the organisation?

The intelligence of organisations lives not just in their intellectual assets but also in their routines (Nelson & Winter), in the boundaries they set and maintain (Star & Bowker), and in the everyday improvisations and tacit skills that ethnographers have long documented (Orr). Other presenters at EPIC pointed out that the expertise held by teams is not necessarily known even to their managers.

AOI asks: how do we stitch knowledge objects, rules, routines, and practices together in ways that are machine-readable, without flattening them into a single database or losing what makes organisations (and teams) distinct and more than the sum of the individuals involved?

Building the Infrastructure

To achieve AOI, we need to connect two things:

• The systems where organisational knowledge already resides (such as Slack, GitHub, decision-making platforms, policy registers, memos and document repositories).
• The local practices and tacit insights held by teams, community members, and researchers (keeping in mind that these too evolve in conjunction with automation processes).

We cannot get to AOI until the underlying infrastructure exists to connect organisational knowledge objects with the governance of that knowledge. (Note that I use the word governance broadly here: how organisations sort, share and protect their knowledge matters for their ability to survive, stay on mission, and adapt).

That is the focus of our project. BlockScience is building KOI (Knowledge Organisation Infrastructure) as this foundation.

KOI: A Protocol for Organisational Knowledge

KOI connects knowledge where it already lives. It is a modular architecture that involves nodes, which are responsible for an observed space. A node can process what’s going on there, and then pipe it to other nodes. It also involves reference identifiers (RIDs) that can be used to order and describe the knowledge objects that nodes are processing. You can read more about the technical dimensions of KOI on BlockScience’s blog and Metagov’s FAQ.

One consequence is that organisations can share knowledge with each other without conforming to a single ontology. We can connect tools, which might include generative AI, to assist in translating across organisation ontologies.

This matters because every organisation has its own distinctive ways of sorting and governing. AOI depends on preserving those differences.

Rather than moving raw data, KOI helps us label, route, and govern knowledge with its context and rules intact. This makes it machine-readable with enforceable boundaries, which sets the ground for AOI. KOI is therefore a path to regulative rather than generative AI — meaning that organisations can set and enforce boundaries that machines must respect.

Where Ethnography Comes In

Ethnography has always been about observing what is otherwise unseen, about making practices visible. In second-order cybernetics, the observer is part of the system. Our work takes that seriously: ethnographers aren’t just describing AOI, we are co-constructing it through our work.

We have begun using KOI to connect our own collecting, contextualising, and curating of organisational knowledge and events back into the organisations and communities we are working with. What began as tools for ethnography has become experiments in AOI.

In the demo we produced for EPIC, ethnographic data flows through KOI across three organisations: BlockScience, Metagov and ADMS (our research team).

Our developer Luke Miller has two Obsidian vaults running, containing real notes from ethnographers Kelsie Nabben and Brooke Ann Coco. These notes are comments from Slack channels that people have consented to share (using our Telescope bot). The ethnographers have added field notes, links and quotes. We developed an Obsidian plugin that connects the vaults into KOI so that nodes can communicate with each other.

The outcome is that discussions about KOI from Metagov are sent to BlockScience’s KOI node, where they are grouped with ethnographic notes from BlockScience’s own Slack channel and other notes the ethnographers have chosen to share.

The result is a live cybernetic feedback loop, where ethnographic insights become part of the organisation’s intelligence.

AOI depends on such feedback loops: systems that sense, interpret, and act on information in ways that can be governed. Traditionally, AI governance talks about the human-in-the-loop. But that tends to treat the human as a last-minute check — a kind of moral authority.

AOI is about building the loop itself: specifying the pipes, the permissions, the shared practices that make feedback possible.

Implications for Ethnography

As we discuss in our written paper, this changes ethnography itself, with implications for how we think about observation, interpretation and inscription. Importantly, ethnography is no longer about a single authored narrative output, but an ongoing, dynamic and participative knowledge production process.

Conclusion

• AOI is not about replacing organisations with AI, but making their knowledge legible, governable, and usable by both humans and machines.
• But this requires infrastructures like KOI that get us to regulative (not generative) AI.
• Ethnographic practice is central because it observes and inscribes the tacit and invisible, and helps build the loops that make AOI possible.

In one possible future, organisations could maintain their own local systems and practices, operating as a mesh with other organisations so that boundaries are not barriers to knowledge expansion, but help advance the productive capability of group endeavour.

To connect this to Pauliina Rautio’s keynote, what if the organisation here was not a firm but a land regeneration mission, where the contributions of animals, plants and humans are machine-readable? What if you could talk to the place itself? What if such ‘organisations’ could talk directly to each other, learn from each other?

It is still early days for our work. If you are interested in learning more, please reach out.

The paper the talk is based on was co-authored with Kelsie Nabben, Michael Zargham, Jason Potts, Brooke Ann Coco, and based on technical work by Luke Miller and Matthew Green. Elianna DeSota and David Sisson are also core contributors to the project, which is a collaboration between ADM+S, BlockScience and Metagov.

Building the Loop: The Role of Ethnography in Artificial Organisational Intelligence was originally published in BlockScience on Medium, where people are continuing the conversation by highlighting and responding to this story.

This article describes the evolution of the Telescope bot through the KOI protocol. Together, KOI and Telescope turn inputs from informal conversations within online community forums into organisational ordering and memory that can be continuously updated and used to implement procedures and boundaries for automated systems, including LLMs and agentic AI. Scroll to the end for a short demo. To read more about artificial organisational intelligence, see this post.

What is KOI?

Knowledge Organisation Infrastructure (KOI) is an open architecture and protocol developed by BlockScience with input from Metagov. KOI enables groups to organise and share knowledge on their own terms, providing a common framework for diverse groups to indicate what they know and to voluntarily share information without forcing them into a single unified database, closed source software, or sacrificing control over sensitive information [1].

At the heart of KOI are “reference identifiers” (or RIDs), a protocol also developed by BlockScience. RIDs are akin to library-style call-numbers that provide a systematic way to point to something (known as a ‘knowledge object’, such as a chat message, a document, or a spreadsheet) without handing over the item itself. Anyone who has the label can locate and request the original knowledge object to discover what’s in it, yet the label alone never reveals the full content.

KOI nodes look after these labels. A node might collect new labels, update existing ones or remove them when the underlying material changes. When nodes exchange these three simple signals with other nodes — “new,” “update,” or “forget” (FUN) — they weave themselves into a KOI-net. In that network, every participant can refer to and discuss the same pieces of knowledge even if the files live on different servers, are offline/non-digital, or belong to different organisations, as long as they are using shared RID labels.

As such, KOI allows groups to share a common map of “what we know” without forcing anyone to surrender their data or conform to a unified or centralised system or ways of knowing. A KOI node within a KOI network can call data from another node (if permissions are in place to do so), process data into new knowledge objects, or act on other systems, such as outputting to a Slack channel. As BlockScience’s Michael Zargham describes it, KOI is like duct tape and WD-40 that can be used to connect the sites where information is stored and used so that the labels travel but the material stays where it is. It is this toolkit for “stitching together”, as opposed to raw data being hoovered up, that enables groups of people and software agents to coordinate, share knowledge sets, and add boundaries and context over how that occurs. Genuinely processing knowledge is key to understanding the significance of KOI. As David Sisson and Illan Ben-Meir from BlockScience have articulated, knowledge is achieved by continually organising and reorganising information, allowing us to link current actions with potential future outcomes.

As the KOI protocol’s job is to standardise how nodes talk to each other, not what each node does internally, groups remain free to encode their own rules and processes inside their KOI nodes. They can also set policies about what knowledge to share or not share externally by deciding which events to emit or which queries to answer. In the KOI-net beta demonstration, for example, five nodes (a coordinator, two sensors, two processors) self-assemble into a knowledge processing network, but this is just one configuration. Future KOI-nets could incorporate more complex arrangements, including community-defined rules at the node level or higher-level governance where multiple communities connect their KOI-nets with negotiated policies. The Telescope deployment is the first to venture into this territory.

Telescope: From bot to KOI-net

Telescope began as a simple qualitative data collection bot, designed to help overcome challenges that I and others in Metagov were facing in our research into onchain technologies and associated communities. Traditional ethnography involves a human researcher observing and taking notes, which doesn’t scale well to online communities where people can join and leave at any time, remain pseudonymous if they so choose, and where organisational knowledge is distributed across numerous platforms and places (e.g. Telegram, Slack, GitHub, blogs, voting platforms, the blockchain, and more). Lurking and logging conversations under these conditions can raise ethical issues, as participants may not be aware that research is taking place and could (in fact should) be suspicious of messages coming from unknown researchers. Telescope was first deployed as a Discord bot that could be invited into servers (i.e. Slack or Discord chat applications) to assist with data collection (first tested in this ethnography). It is now evolving into a contextual, rule-based knowledge collection infrastructure through the KOI-net protocol.

What sets Telescope apart from simple chat loggers is its consent mechanism and participative capabilities. Rather than vacuuming up all messages, Telescope relies on community members to explicitly flag which content is relevant to the research question. This is done via a 🔭 telescope emoji reaction. If someone in a participating Discord server considers a particular message to be important to the research, they can react to that message with the telescope emoji. The bot detects this and initiates a consent workflow: it will send a polite request via direct message to the author of the message, providing them with information about the project and asking for permission to include their content in the dataset. Only if the author gives consent will Telescope save that message to the research dataset and add it to a dedicated channel featuring approved collected posts where others can see the dataset evolve. Telescope can either be used by a researcher or by the organisation itself to crowdsource the curation of a qualitative dataset. In the latter, community/organisation members surface the insights they find noteworthy, a moderator approves the bot to make the request, and authors approve their inclusion. As we noted in this paper, Telescope can help communities preserve and gain a deeper understanding of their own history, as the collected discussions can later be analysed or reflected upon by the community itself, not just the researchers.

My early experiments with Telescope revealed its limitations, which we have begun addressing through KOI. Because a telescoped message can be misleading or lack meaning in isolation, it is the ethnographer’s job to situate the comment in its original context and connect it to broader themes and events. With the help of Matthew Green (Research Assistant at RMIT University), I began using Obsidian to import Telescope into Markdown files with specific fields to add contextual data (standardised using Obsidian’s template function) and links to other notes.

As we could not elegantly import Telescopes into Obsidian, we asked Metagov and BlockScience developer Luke Miller (who led the creation of Telescope and is now leading development of the KOI-net protocol) to build an Obsidian plugin. Luke suggested we first create a KOI node that receives Telescope data and sends only the approved messages to the researchers’ vaults, keeping any unapproved messages and data required for processing private. The resulting system enables the ethnographer to receive Telescopes into individual notes where they can easily add field observations, links and additional metadata. These Telescope notes can then be structured into maps of content along with other notes (including highlights and notes imported from reading software such as Zotero and Kindle, or content clipped from the web). The plugin syncs Telescope data and ensures that only those who have a key can access the data in the KOI node.

We have now further adapted the plugin so the ethnographer can send any kind of note (Telescope notes plus additional context, links, etc.) back to a KOI node and specify how it spreads through the network. For instance, a researcher using a KOI-augmented Obsidian vault can send notes that include the added ethnographic observations to other researchers on the team or to the community. How the community uses that data is then up to them. One possibility is that participants could query a collection of ethnographic observations using an LLM chatbot — essentially enabling community engagement with the research as it is happening. Alternatively, an LLM agent could subscribe to KOI events to receive live context as it is sent from the ethnographers, to help provide context for decision-making or feedback on a specific topic (see below).

Enacting Telescope

In summary, Telescope and KOI can help make organisational knowledge and its governance machine-readable so that it can be interacted with, including through AI. The trajectory of this capability as it progresses looks something like this:

1. Telescope’s initial consent model is the first layer of governance in that no data enters the system without an individual’s permission.
2. The community can specify policies for the Telescope sensor node. For instance, a DAO might collectively approve sharing a certain subset of Telescope-collected insights with another DAO alliance.
3. Community members could join in analysis, developing an organisational reflective practice. Members might see patterns of their own activity (for example, “this week, discussions clustered around these themes”) derived from KOI-curated data. This could enhance self-awareness in organisations or communities and help them adapt governance in real-time. Because KOI can integrate with analytic tools and even AI, these insights could be generated automatically, but with clear provenance. Telescope and KOI can thus create real-time feedback loops between observation and action.
4. Telescope could also be used as a form of contribution system by capturing contributions of knowledge through their reception. A contribution occurs not when the person adds the comment, but when someone else registers its importance by adding the telescope emoji or extending on the original post (like a citation). KOI’s RIDs make it easier to trace a piece of knowledge back to its origin and the chain of sorting and response (who collected it, who processed it). The community collectively filters and curates their knowledge, building a record of what is important (value creation) in the process.
5. KOI’s ability to add context to a node provides a means to create checks-and-balances for autonomous agents and encoding context and procedures that the node is required to follow. This paves the way for AI integration that’s operating within community-set constraints.

Conclusion

In April, Benjamin Bratton, a Professor of Philosophy of Technology, posted OpenAI o3’s definition of “humans” to the social media platform X: “A self-modifying swarm of molecule-sized archivists that coax entropy into meaning by wrapping fleeting moments in elaborate chains of memory, prediction, and ritual”. If this is how an LLM ‘views’ us, KOI-nets are an upgrade that makes a variety of such processes more machine-readable and controllable on the human side. o3’s definition captures how we humans create meaning through culture and then stabilise, scale, preserve or communicate it through organisations, systems and infrastructures. KOI-nets support this pattern-preserving impulse, making our group knowledge systems interoperable without compromising our control over our groupish human methods (rules, labels, organisations) for avoiding informational decay and entropy.

Demo

Credits

You can view the git repositories for KOI here, for Telescope here, and Obsidian vault here.
Thanks to the following people for input into this post and for ongoing collaboration on the tools and concepts discussed.

ADM+S (RMIT node) researchers: Ellie Rennie, Matthew Green, Kelsie Nabben, Brooke Ann Coco. Jason Potts is leading the theory component of the project.

BlockScience: Michael Zargham, Luke Miller, David Sisson

Metagov: Elianna DeSota

[1] For insight into why we use the term ‘knowledge’ rather than ‘data’, read this piece by BlockScience’s David Sisson and Illan Ben-Meir.

Building Feedback Loops for Organisational Intelligence: Telescope and KOI in action was originally published in BlockScience on Medium, where people are continuing the conversation by highlighting and responding to this story.

This paper arose out of the need to consider attention as a (scarce) resource in governance in digital contexts. It was co-authored with colleagues via Metagov (a not-for-profit research collective).

Attention economies = the analysis of human attention as a resource.

Governance surface = the means available for organisational adaptation and action.

We found that existing theoretical frameworks for the governance of community-managed resources (such as Ostrom’s rules for managing the commons and others) lack adequate consideration for how people’s attention is engaged, directed, and sustained over time.

To address this gap, this paper puts forward heuristics that assess how attention relates to governance in online organisations. The heuristics serve as analytical and normative tools to enable researchers and system designers to better understand attention in a governance system. They invite consideration of whether the structure of attention in a system is appropriate, efficient, and just. The heuristics are informed by literature on attention economies and governance, as well as three case studies that consider recent attempts to address attention in the design of governance surfaces in blockchain-based systems.

Read the full paper at: https://osf.io/preprints/mediarxiv/cdrmp_v1

Originally published at https://kelsienabben.substack.com.

New Paper: Online Governance Surfaces and Attention Economies was originally published in BlockScience on Medium, where people are continuing the conversation by highlighting and responding to this story.

Multi-Agent Influence Diagram (MAID), Generalized Dynamical Systems (GDS), & Complex Adaptive Dynamics Computer-Aided Design (cadCAD)

This series of four interactive workshops explores the compatibility and synergies of three interconnected modeling frameworks. It develops a conversational framework to explore and consider theoretical and practical applications for model and tool selection and their use together for rigorous, coherent, and efficient modeling of complex systems.

Led by Octopus, a Blockscience Research Engineer, this workshop series is designed to be broadly accessible to researchers, engineers, and teams comfortable with scientific computing in Python and familiar with or interested in modeling dynamic games. Required mathematical literacy includes linear algebra, dynamical systems, and event-driven systems. Knowledge of real analysis, optimization, the existence and uniqueness of equilibria, multi-agent systems, and potential games will aid your understanding.

In this post, we first provide a brief introduction to each of the three modeling tools: Multi-Agent Influence Diagrams (MAIDs), General Dynamical Systems (GDS), and complex adaptive dynamics Computer-Aided Design (cadCAD). Next, we provide a chronological overview of the series’s four (4) workshops, with links to additional resources.

• Workshop 1: Introduction & Overview
• Workshop 2: Explorations & Connections
• Workshop 3: Agents, Strategies, & Equilibria
• Workshop 4: The Modeling Process & Future Directions

Caveat

⚠️ Throughout the series, you may notice that we touch on relevant technical concepts and alternative approaches only briefly, choosing to focus on the application of MAIDS to inform modular model structures and optimization problems. For example, in the third workshop, we avoid established algorithms for finding Nash Equilibria. Instead, we focus on finding Nash Equilibria via optimization processes, treating this as a black-box optimization problem without a special structure.

Multi-Agent Influence Diagrams (MAIDs)

MAIDs are a diagram syntax useful for representing processes that can be understood as multi-stage games. As diagrammatic models of multi-stage games, they represent — agents, state variables, decision rules, chance processes, and utilities.

Introduced in the paper “Multi-Agent Influence Diagrams for Representing and Solving Games” by Koller and Milch (2003), MAIDS builds on prior work, including influence diagrams, which are themselves generalizations of Bayesian networks. Both influence diagrams and Bayesian networks typically examine randomized probability distributions that consider the actions of one agent. Intended to capture the influence between multiple agents, MAIDs extend on previous models in two ways; MAIDs deal with discrete agents rather than just one overall process and look at decisions they might make in response to different events.

The traditional representations of games using the extensive form or the strategic form obscure much of the structure of real-world games. In this paper, we propose a graphical representation for noncooperative games — multi-agent influence diagrams (MAIDs). The basic elements in the MAID representation are variables, allowing an explicit representation of dependence, or relevance, relationships among variables. We define a decision variable D′ as strategically relevant to D if, to optimize the decision rule at D, the decision maker needs to consider the decision rule at D′. We provide a sound and complete graphical criterion for determining strategic relevance. We then show how strategic relevance can be used to decompose large games into a set of interacting smaller games, which can be solved in sequence. We show that this decomposition can lead to substantial savings in the computational cost of finding Nash equilibria in these games.

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Koller, D., & Milch, B.(2003) Multi-agent influence diagrams for representing and solving games, Games and Economic Behavior, Volume 45, Issue 1, 2003, Pg 181–221, ISSN 0899–8256, https://doi.org/10.1016/S0899-8256(02)00544-4.

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Additional MAIDS Resources
Multi-Agent Influence Diagrams for Representing & Solving Games
Equilibrium Refinements for Multi-Agent Influence Diagrams
MAIDS for DeFi Governance
Exploring MAIDs as a family of Process DAG’s

Generalized Dynamical Systems (GDS)

Generalized Dynamical Systems (GDS) were introduced by Barbashian (1949), further developed by Roxin in 1967, and extensively researched here at BlockScience as a mathematical framework for applications in the emerging area of enforceable contracts.

Building upon control theory, the GDS framework provides a ‘template’ for dynamical systems whose states are influenced by decision-making. The approach emphasizes the impact of feasibility and admissibility of actions on solution trajectories instead of relying upon fixed (potentially restrictive) assumptions about decision-maker behavior.

In the first of three works, we consider a generalized dynamical system (GDS) extended from that initially proposed by [25, 24], where a data structure is mapped to itself, and the space of such mappings is closed under composition. We argue that GDS is the natural environment to consider questions arising from the computational implementation of autonomous and semi-autonomous decision problems with one or more constraints, nesting into one framework well-studied models of optimal control, system dynamics, agent-based modeling, and networks, among others. Particular attention is paid to mathematical constructions which support applications in mechanism design. The contingent derivative approach is defined, along with an associated metric, for which a GDS admits the study of existence of state trajectories that satisfy system constraints. The system may also be interpreted as a discretized version of a differential inclusion, allowing the characterization of the reachable subspaces of the state space, and locally controllable trajectories. The second and third parts in the three-part series are briefly described and cover applications and implementations, with the latter demonstrating explicitly how a GDS can be implemented as software using Complex Adaptive Dynamics Computer Aided Design (cadCAD) [30].

📚

Zargham, M., & Shorish, J. (2022). Generalized Dynamical Systems Part I: Foundations. WU Vienna University of Economics and Business. Working Paper Series / Institute for Cryptoeconomics / Interdisciplinary Research https://doi.org/10.57938/e8d456ea-d975-4111-ac41-052ce73cb0cc

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Additional GDS Resources
A Brief Introduction to Generalized Dynamical Systems
GDS Definitions & Quick Reference
GDS Statespace Walkthrough

complex adaptive dynamics Computer-Aided Design (cadCAD)

Developed by Blockscience, cadCAD is an open-source Python package that assists in the processes of designing, testing, and validating complex systems through simulation.

Given a complex system model, cadCAD can simulate the impact that a set of actions might have on it. This helps users make informed, rigorously tested decisions on how best to modify or interact with the system to achieve their goals. cadCAD supports different system modeling approaches and can be easily integrated with common empirical data science workflows. Monte Carlo methods, A/B testing, and parameter sweeping features are natively supported and optimized.

Complex Adaptive Dynamics Computer-Aided Design (cadCAD) is a language for encoding Generalized Dynamical Systems (GDS) as computer programs. As a modeling framework, cadCAD is based on best practices from control systems engineering; this framework was designed by Michael Zargham, Markus Koch, and Matt Barlin in 2018 in order to bypass the need for powerful closed-source software such as Matlab/Simulink in their economic systems design work at BlockScience. The first implementation of cadCAD was developed by Joshua Jodesty, in parallel with the framework development. Python was chosen for the initial implementation due to the wide range of numerical computing tools already available in Python’s open-source data science ecosystem. The Python implementation of cadCAD was Open Sourced in partnership with the Commons Stack in 2019 and has since exploded in use, most notably amongst cryptoeconomic systems designers and token engineers. As of May 2021, there are multiple implementations of cadCAD. This specification was written in part to document the unifying principles for multiple cadCAD implementations spanning Python, Javascript, and Rust and in part to serve as the basis for a new Julia implementation of cadCAD for which Emanuel Lima is the lead developer.

📚

Zargham, M., & Lima, E. (2021). cadCAD Formal Specifications. https://www.scribd.com/document/637260442/cadCAD-specification-document

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Additional cadCAD Resources
cadCAD Documentation
Differential Specifications Syntax Key

Workshop 1: Introduction & Overview

Workshop 1 introduces MAIDs, GDS, and cadCAD modeling tools. The Killer Tree scenario from Koller and Milch (2003) introduces MAID diagrammatic syntax. The resulting underlying graph is then used to begin exploring the connections between the three different types of modeling frameworks, with a focus on their use within Blockscience’s work.

PART I: Multi-Agent Influence Diagrams (MAIDs)
[00:00:00] Introduction Overview & Goals
[00:03:10] Killer Tree Scenario: Meet Alice & Bob
[00:06:20] Killer Tree Scenario: MAIDs Representation
[00:08:30] Killer Tree Scenario: Discussion of Diagrammatic Syntax
[00:10:58] Killer Tree Scenario: Discussion of Utility Function & Total Utility
[00:14:54] Discussion: Modeling Perspectives and the Role of Nature
[00:18:02] Alt MAID Representation
[00:19:01] MAID Mathematical Definition
[00:20:35] MAID application to Graphs & Hypergraphs
[00:23:25] Discussion: MAIDs Where to Next?

PART II: Generalized Dynamical Systems (GDS)
[00:34:17] Choices of State Space
[00:41:18] Modeling Decisions & Trade-Offs
[00:43:08] Incorporating Time in Dynamic Systems
[00:43:48] Generalized Dynamical Systems and Input Spaces
[00:46:02] Time, Events & Parallel Processing
[00:51:40] Reconciling Models & Information Flow
[00:55:24] Translating MAIDs to GDS Models

PART III Complex Adaptive Dynamics Computer-Aided Design (cadCAD)
[00:59:28]Structuring cadCAD Models from MAIDS
[01:04:51] The Role of Domain Experts in Modeling
[01:05:25] The Importance of Modularization in Modeling
[01:07:25] System Identification & Observing Behavior
[01:13:33] Final Thoughts and Future Directions

⚙️

Workshop 1: Resources
Slides Workshop 1: Introduction & Overview
A Practical Theory of Fungibility

Workshop 2: Explorations & Connections

Workshop 2 takes a more technical look at complex system modeling in terms of both theoretical mathematics and practical coding. The mathematical perspective establishes a rigorous footing for the idea of connections between MAIDS, Influence Diagrams, and Bayesian Networks. The practical approach considers tools to make coding simulations more reliable, fast, and translatable between different models.

PART I: Review of MAID Graph Structure
[00:00:00] Practical & Theoretical Approaches
[00:01:47] MAIDS Graph Structure
[00:05:50] Technical Discussion
[00:08:33] Distinctions GDS & MAIDS
[00:11:20] Bayesian Networks (MAIDS) vs Dynamical Systems (GDS)
[00:17:12] Model & Tool Selection

PART II: Theoretical Foundations
[00:20:36] MAIDs as a Generalization of Influence Diagrams
[00:27:12] Lenses: Category Theory & Mathematical Modeling
[00:37:10] Choice in Modeling Transformations
[00:40:55] Practical Application: Utility in Decentralized Protocols
[00:43:43] Aggregation
[00:47:00] Bayesian Networks: Conditional Probability Distribution
[00:51:18] Conditional Independence
[00:58:52] Causal Inference & Modeling
[01:01:55] Active & Blocked Paths

PART III: Practical Applications
[01:08:47] Engineering for Uncertainty
[01:11:19] Directional Separation
[01:14:06] Compute Directional Separation
[01:16:05] Sanity Checks & Cross-Validation
[01:17:11] MSML as a Rosetta Stone
[01:19:29] Causality & System Identification
[01:24:35] Concluding Thoughts and Future Directions

⚙️

Workshop 2: Resources
Slides Workshop 2: Explorations & Connections
github.com/BlockScience/MAIDs
Math Spec Modeling Language (MSML)

🎮

Generative Meta Game
This GitHub Repo contains collaborative research exploring iterative metagames where instances of game state are represented as MAIDs, and each player evolves the game via “game mining” moves. Since each state in the state space is actually a game, we are experimenting with “second-order” Generalized Dynamical Systems (GDS), which is implementable by making cadCAD configurations states of cadCAD models.

Workshop 3: Agents, Strategies, & Equilibria

Workshop 3 focuses on the notion of a game strategy profile, wherein the strategy chosen by each player ultimately determines the agent utilities and possible game outcomes. Workshop participants discuss theoretical and practical optimization approaches with pgmpy & cadCAD. We show how to determine a Bayesian Network using the Killer Tree MAID and use pgmpy to sketch the algorithm for finding a Nash Equilibrium as described by Koller and Milch. See ⚠️Caveat

PART I: Multi-Agent Influence Diagrams (MAID) Review
[00:00:00] Introduction
[00:00:43] Review of Multi-Agent Influence Diagrams (MAIDs)
[00:01:27] Killer Tree Scenario: Alice & Bob’s Decision-Making Scenario
[00:02:21] Clarifications & Underlying Graph Structure of a MAID

PART II: Decision Rules, Strategies & Optimization
[00:06:18] Decision Rules & Strategies
[00:07:31] Interactive Q&A on Decision Rules
[00:19:39] Understanding Strategy Profiles
[00:21:50] Conditional Probability Distributions
[00:25:47] Connecting MAIDS Agent Strategies & GDS
[00:27:19] Optimization & Strategy Selection
[00:35:06] Modeling Decisions in Utilities, Strategy Profiles & Optimization.
[00:43:01] Understanding Decision Rules & Strategies
[00:44:06] Control Policies & Observable States
[00:45:06] Challenges in Traditional Economic Game Theory
[00:47:13] Equilibrium in Control Theory
[00:48:38] Nash Equilibrium & Strategy Updates
[00:51:10] Dynamical Systems
[00:55:43] Starting Points, Strategy & Maximizing Utility Value

PART III: Coding Optimization
[01:00:07] Determine Bayesian Network using MAIDS
[01:01:54] Implementing Bayesian Networks in Python
[01:05:18] Conditional Probability Distributions
[01:11:35] Optimizing Decision Rules
[01:16:37] Algorithm for Finding Nash Equilibrium
[01:19:29] Multi-Scale Evolving Systems & Network Games
[01:26:39] Concluding Thoughts & Future Directions

⚙️

Workshop 3: Resources
Slides Workshop 3: Agents, Strategies & Equilibria
Algorithmic Proofs for Finding Nash Equilibrium
From Nash to Lyapunov
MACIDs: Multi-Agent Causal Influence Diagrams
PyCID: Causal Influence Diagrams Library
Causal Incentives Working Group
Tutorial: Payoff dynamics & higher-order learning in population games

Workshop 4: The Modeling Process & Future Directions

Workshop 4 explores how MAIDs can inform cadCAD models, emphasizing a standardized structure for policy functions and state update functions, a process that offers a template for encapsulating agent logic, enabling greater abstraction and testability. In this way, MAIDs could allow non-coders to participate actively in creating the cadCAD model structure or aid translation by AI. See ⚠️Caveat

PART I: Introduction & MAIDs Review
[00:00:00] Introduction and Greetings
[00:00:00] Session Overview and Background
[00:00:48] Multi-Agent Influence Diagrams (MAIDs)
[00:03:46] Personal Background and Research Approach
[00:05:34] Undergraduate Research and Team Building
[00:07:14] MAIDs in Mathematical Pedagogy

PART II: MAIDS Informed CAD CAD Frameworks & Code Structures
[00:10:25] Modeling Frameworks & CAD CAD
[00:16:37] What MAID representations enable
[00:18:45] CAD CAD Modeling
[00:20:09] Value of Qualitative Representations & Graph Structure of MAIDs
[00:21:43] Translating MAIDs to cadCAD Model Structure
[00:22:50] cadCAD Partial State Update Blocks
[00:24:32] Modeling Process

PART III: MAIDS Informed Agent-Based Approach to cadCAD
[00:28:36] Coding Framework for cadCAD
[00:31:20] Connection to MSML
[00:33:42] Agent Decisions
[00:35:25] Agent Strategy
[00:36:10] Agent Utility Functions
[00:37:14] Possibilities of Agent Structure
[00:42:06] Discussion: Q&A
[00:44:26] cadCAD World Model
[00:46:46] Board Game Analogy: MAIDS & cadCAD
[00:49:24] Complexity in State Space & Rule Sets
[00:51:54] Decision Rules & Strategy Space
[00:54:52] Agent-Based Modeling & Network Policies
[00:58:13] Agent Modeling as a Philosophy
[00:59:21] Space & Time, Agents & Abstract Patterns
[01:01:48] GDS & Mapping the Admissible Action Space
[01:02:53] GDS for Non-Trivial Choice
[01:06:05] Combining Rules into Strategies
[01:07:13] Agents as Parameters
[01:08:40] Parameters to Simulations to Experiments
[01:09:16] Designing Experiments
[01:09:56] Philosophical Questions on Passive Agents

PART IV: Applied & Future Directions
[01:14:43] Practical Engineering Application
[01:16:14] The Search for Nash Equilibrium
[01:21:55] Learning Rules & Second Order Systems
[01:23:21] Dynamic Games & Strategy Updates
[01:31:17] Final Thoughts & Future Directions

⚙️

Workshop 4: Resources
Slides Workshop 4: The Modeling Process & Future Directions
Block Diagrams for Categorical Cybernetics
Differential Specifications Syntax Key

Future Directions: Thoughts & Questions

These workshops raised a number of possibilities and intriguing questions that we hope to explore more in the future.

• How can the Agent-Based principles of MAIDs be used to structure cadCAD models, especially for teams that need structured low-demand coding practices?
• How do we operationalize the structure of MAIDs as (hyper)graphs for structural search and long-term organizational memory?
• How can optimization and search be used to improve the spaces of Games and Agents and find parameter settings efficiently?
• How can we better use modeling to understand the inherent geometry of both the map and the territory it hopes to represent?

Acknowledgments

A massive thank you, Octopus! for developing and leading this workshop series. Thank you, workshop participants, for asking questions, challenging ideas, and sharing your perspectives, which helped to make this an interactive and collaborative learning experience. Dr. Zargham and Dr. Shorish for the domain expertise upon which we built. RJ Cordes for informing the workshops, Nick Hirannet and folks in the Slack channel, and the communications team for publishing. And you! Thank you for watching and reading, learning, and sharing — all of us nodes in the open-source knowledge networks of our collective future.

About BlockScience

BlockScience® is a complex systems engineering, R&D, and analytics firm that integrates ethnography, applied mathematics, and computational science. We analyze and design safe and resilient socio-technical systems, and with deep expertise in market design, distributed systems, and AI, we provide systems engineering services to a wide range of for-profit, non-profit, academic, and government clients.

🌐 Website | 🐦 Twitter | 📚 Medium | 👻 Blog | 🎥 YouTube | 👥 Linkedin

Exploring Complex Systems Modeling with MAIDs, GDS & cadCAD was originally published in BlockScience on Medium, where people are continuing the conversation by highlighting and responding to this story.

Welcome to the BlockScience Quarterly newsletter — autumn edition! 🍂 As the season changes, we’re excited to share our latest work on emerging technologies, innovation, and their impact on society.

In this edition, we turn the lens inward, exploring how BlockScience operates as a cybernetic organization, applying systems thinking to build adaptive, resilient institutions. We also dive into groundbreaking research on data intermediary networks and explore the delicate balance between simplicity and complexity in digital infrastructure through foundational principles like Occam’s Razor and Ashby’s Law.

Additionally, we introduce a new grant program for researchers to investigate algorithmic policymaking in Web3 and invite you to explore the evolving role of trust and governance in decentralized systems. With contributions from our experts on economic systems and the intricate balance of norms and institutions, this collection provides a comprehensive look at how technology is shaping the future.

Papers & Articles

BlockScience as a Cybernetic Organization (Cyb-Org)

In this article, we reflect on our own organization and explore how we use systems thinking and organizational analysis at BlockScience. By mapping our internal structure, we share insights on building more adaptive, resilient organizations in complex environments.

Data Intermediary Networks

In this article, Dr. Kelsie Nabben, senior social scientist, shares insights from her recent research on “data intermediary networks.” She explores how these networks, built on data mesh architecture, foster cooperation among data operators and unlock new value in decentralized data ecosystems.

Grant Program Announcement: Empirical Investigations of a Digital Frontier

OpenTeams Incubator, BlockScience, and Token Engineering Commons have launched a small grants program. The program invites data and computational scientists to explore algorithmic policy-making in Web3 through empirical research. Apply your expertise to this digital frontier and submit your proposals today here.

Norms, institutions, and digital veils of uncertainty — Do network protocols need trust anyway?

In this paper, Eric Alston explores how norms and institutions interact with digital governance, focusing on the uncertainty faced by networked organizations. Alston argues that while institutions provide structured rules to mitigate “known unknowns” by defining behavior and penalties, they remain incomplete in addressing the full range of unpredictable events and human actions. Norms, which shape behavior automatically within communities, serve as a complementary mechanism to fill these gaps, particularly in periods of uncertainty. The paper highlights how digital organizations must integrate both institutional flexibility and community-driven norms to adapt to the complexities of networked environments.

Videos & Talks

Knowledge Organization Infrastructure

In this video, Orion Reed and Luke Miller dive into the Knowledge Organization Infrastructure (KOI) we’ve been developing. You’ll learn about key design principles, like reference identifiers (RIDs), digital objects, and temporal identity, all aimed at improving how we organize and manage knowledge. Our approach emphasizes the importance of referencing existing knowledge rather than recreating it, supports multiple perspectives, and avoids relying on a singular version of truth.

What do people mean when they say economics? w/ BlockScience Chief Engineer Dr. Michael Zargham

In this short video clip, Dr. Michael Zargham shares his insights on the meaning of “economics” during the June 2024 meeting of The Open Source Science Economics Interest Group, where he serves as Co-Chair. Drawing from the New York Federal Reserve’s “Fields of Study in Economics,” Zargham explores the broad scope of economic fields and emphasizes the need for innovative approaches, such as computational methods and open-source practices, to drive economic impact. You can watch the entire seminar here.

Ecosystem

Killswitch Protocols Or On Engineering Recursive System Death

In this paper, our senior researcher Eric Alston, along with Seth Killian, and Garrette David, wrote this article exploring the concept of “killswitch protocols” — the mechanisms designed to intentionally stop systems that could spiral out of control. Using examples from nuclear reactors to online communities, they ask critical questions about when and how systems should be shut down, and who holds the power to execute these failsafes. This piece navigates the complex tradeoffs involved in killswitch design, governance, and the inevitable shortcomings of these protocols in both digital and physical worlds.

An Ethnographic Sublime in The Land of the Unreal (Review Essay)

This review essay explores Lisa Messeri’s ‘In the Land of the Unreal,’ an ethnography that reclaims a sense of place within the seemingly boundless realm of virtual reality. The book juxtaposes the landscapes of Los Angeles and the immersive worlds of VR, examining their social, technological, and gendered implications. In addition to Messeri’s work, the essay highlights the contributions of Kelsie Nabben and Michael Zargham, whose ethnographic research titled The Ethnography of a ‘Decentralized Autonomous Organization’ (DAO): De-mystifying Algorithmic Systems’, which sheds light on how blockchain and DAOs may offer new forms of social and organizational engagement, or fall into familiar traps of inequality and unrealized promises. Together, these works provide a critical lens on how the convergence of the virtual and real can reshape understandings of place, identity, and technology’s social potential.

Fundamentals of Systems Engineering

In Episode 6 of the podcast, Fundamentals of Systems Engineering, Andrew Clark and his team explore how AI and data science can benefit from systems engineering. They discuss building robust AI models, the importance of safety and criticality, and how systems thinking from Bell Labs to NASA’s Apollo program can improve AI innovations and business decisions.

🙏 Thank you for your very valuable attention and stay tuned for the next quarterly newsletter.

From our team to yours… Enjoy the vibrant colors of autumn! 🍂

ABOUT BLOCKSCIENCE

BlockScience® is a complex systems engineering, R&D, and analytics firm. By integrating ethnography, applied mathematics, and computational science, we analyze and design safe and resilient socio-technical systems. With deep expertise in Market Design, Distributed Systems, and AI, we provide engineering, design, and analytics services to a wide range of clients including for-profit, non-profit, academic, and government organizations.

Q3 Newsletter: Cybernetic Organizations, Data Networks & the Complexity of Digital Infrastructure was originally published in BlockScience on Medium, where people are continuing the conversation by highlighting and responding to this story.

Welcome summer! 🌞 In this edition honoring the recent solstice, we invite you to learn about the groundbreaking Windfall Protocol for energy efficiency, read about bioregional financing, ecological institutions, and join our adventurous engineer Danilo Lessa Bernardineli on a data-filled bike-packing journey across Brazil’s Bocaina Massif — including his brush with extreme weather. We also share our explorations in knowledge networks and infrastructure, economic mechanisms, polycentric governance, and AI.

For those on the go, check out our recent podcasts and talks on a range of intriguing topics from Orion Reed on Spatial Canvases, software interfaces, and information substrates to underground journeys with Jeff Emmett and Jessica Zartler as they discuss their new book on mycelial networks and biomimetic economic systems in a new podcast.

Speaking of mushrooms… Did you hear the joke about fungus?🍄
You may not like it at first, but it will definitely grow on you 😂

BlockScience Course: Quantitative Foundations for Institutional Engineering and Design

Our team is considering offering a course on the application of Model-Based Systems Engineering to Institutional Design to launch later this year. We would love to hear your feedback in a 3-minute survey to gauge your interest in course attendance and/or sponsorship.This course, Quantitative Foundations for Institutional Engineering and Design, would provide the mathematical foundations necessary for applying management science (especially operations research) techniques to the engineering design process for mechanisms, markets, and institutions through the lens of decision-making and decision support systems. You can find more details about the course duration, materials, intended audience, etc., in the survey. We appreciate your feedback!

Net-Zero Protocols, Field Adventures & Data Science

The Windfall Protocol: Blockchain-Based Infrastructure to Accelerate Building Retrofits & Energy Efficiency Initiatives
Our latest report outlines the Windfall Protocol — a new shared layer of coordination using blockchain smart contracts and data privacy to drive energy efficiency in buildings, crucial in reaching #netzero targets. With a planned pilot for 200,000 homes in Canada, it addresses coordination gaps in labeling, auditing, and financing, enhancing community collaboration accelerating energy retrofits, and could become a key protocol in decarbonizing buildings around the world.

Exploring MycoFi: Mycelial Design Patterns for Web3 and Beyond
Our team members, Jeff Emmett and Jessica Zartler, published a new book: “Exploring MycoFi: Mycelial Design Patterns for Web3 and Beyond,” and were featured on The Green Pill Podcast. In the interview, they explore how mycelial networks can inspire new economic models, drawing parallels between mycelium networks and digital ecosystems in Web3.

Explore Brazil’s Bocaina Massif

Join our intrepid senior research engineer and atmospheric physicist Danilo Lessa Bernardineli on a thrilling four-day bike-packing odyssey across Brazil’s Bocaina Massif. In the true spirit of scientific exploration, he not only documented his journey but tracked all of his data!

Governance & Social Science

Polycentric Privacy Governance in the Digital Age
Discover how encryption and zero-knowledge proofs can balance privacy rights with institutional enforcement. In a new pre-print from Sofía Cossar and our senior researcher, Eric Alston, they delve into the dynamic interplay of technology, institutions, and private choice in the digital age.

Defining Web3: A Guide to the New Cultural Economy: Volume 89
In the book Defining Web3: A Guide to the New Cultural Economy, Volume 89 features contributions by Kelsie Nabben and Eric Alston. Nabben’s chapter, “Entering the Field of Web3: ‘Infrastructuring’ and How to Do It,” introduces Web3 as participatory digital infrastructures offering shared ownership and algorithmic governance. Alston’s chapter, “When Digital Carnival? Distributed Control of the Metaverse Asset Layer to Enable Creative Digital Expression to Flourish,” explores the role of non-fungible tokens (NFTs) and decentralized governance in fostering creative expression within the metaverse.

Governance Modules Library GML Project
Thanks to support from the Stellar Community, we are expanding the Governance Modules Library (GML) project with two pioneering governance modules: conviction voting and osmotic governance. These additions aim to revolutionize community decision-making and foster inclusive governance in decentralized autonomous organizations (DAOs). Co-developed with StellarOrg, the GML offers a versatile collection of computational governance tools designed for various applications including power attribution, identity management, and rights governance.

We extend special thanks to Dani Mimm from Blockful for her invaluable insights on on-chain reputation systems, crucial for addressing plutocracy in DAOs and promoting inclusive governance through Valocarcy (effort-based governance). Stay tuned to learn about upcoming calls and opportunities to participate in shaping the future of governance with GML!

Economic Mechanism & Market Design

Subspace x BlockScience: Parameter Selection Workshop
This video showcases a workshop from our recent collaboration with Subspace on their token economics design. It offers a detailed walkthrough of computational models and simulations used to analyze and recommend parameters for Subspace’s dynamic token issuance mechanisms. Presenters Peter Hacker, Shawn Anderson, and Danilo Lessa Bernardineli discuss the stock-and-flow model, cadCAD framework, system variables, parameters, and analytical methodologies for mapping goals to quantifiable KPIs and exploring trade-offs between parameter configurations.

Knowledge Networks & AI

LLMs & Data Organization: A Demo from Michael Zargham & Orion Reed
Discover the cutting-edge world of knowledge organization and infrastructure with Michael Zargham and Orion Reed. Their short talk dives into the intricate balance of LLMs and data organization, offering insights on building scalable systems that bridge communities and organizations. Explore how their innovative approach transforms digital governance and sets the stage for future decentralized collaborations.

Spatial Canvases: Towards an Integration Domain for HCI, with Orion Reed
In this presentation Orion Reed explores a vision of seamless integration for user interfaces using spatial canvases. This tooling has huge potential for co-working digitally in real-time and automating work flows. Highlighting the benefits of a foundational spatial canvas, he addresses challenges like nesting and layout, aiming to reimagine windowing systems and enhance user interactions through spatial computing.

A Language for Studying Knowledge Networks: The Ethnography of LLMs
Explore alternative visions of Large Language Models in the latest work of our senior social scientist, Kelsie Nabben. Focusing on knowledge creation, infrastructure, and interoperability, this study outlines a research agenda that prioritizes localized, collaborative networks over monolithic models.

SEALing Crypto Security: A Web3 Information Sharing & Analysis Center (ISAC)
“What is different about SEAL is their ability to get the right people, in the right place, at the right time.” In this article, Kelsie Nabben introduces the SEAL-ISAC initiative aimed at strengthening cybersecurity across the cryptocurrency ecosystem against growing threats. Led by the Security Alliance (SEAL), it consolidates threat intelligence bolstering defenses against evolving cyber threats like exchange hacks and fraud. Members gain from real-time information sharing, incident coordination, and best practice guidelines. With leading industry players like Coinbase and UniSwap involved, SEAL-ISAC pioneers collaborative defense strategies, protecting billions in crypto assets.

Ecosystem & Partner Publications

Ecological Institutions → Protocols to Grow Autonomous and Convivial Ecological Actors
A rooted research piece on Ecological Institutions by our research colleague Austin Wade Smith of Regen Foundation in collaboration with Earth Law Center. The article explores ecological institutions that integrate humans and non-humans in governance to enhance environmental stewardship. It introduces “convivial ecological institutions,” which combine community-driven governance with data and social oracles to create a symbiotic relationship between humans and ecosystems, aiming to foster mutual support and regeneration in socio-ecological systems. Check out the article or listen to Wade Smith’s interview with Ma Earth.

Bioregional Financing Facilities
We are excited to share the launch of a transformative book on bioregional finance authored by Samantha Power and Leon Seefeld of Dark Matter Labs, with contributions from Jessica Zartler and insights drawn from the BlockScience team’s research. This publication advocates for a new layer in global finance aimed at regenerating our planet, proposing decentralized resource governance, systemic change projects, and a shift toward a regenerative economy guided by living systems principles and Indigenous wisdom. Accompanying this initiative is The BioFi Project, co-founded with Edward West, which facilitates the implementation of these innovative financial models across bioregions worldwide. Learn more at www.biofi.earth.

Market Layer: Thriving in the Open Liquidity Environment
Fantastic work by our research colleagues at Ecofrontiers. They just released a chapter from their upcoming “Green Crypto Handbook.” Authored by Louise Borreani and Pat Rawson, this chapter explores green cryptomarkets within the evolving Web3 landscape. It emphasizes the intersection of green assets with decentralized finance, aiming to enhance the funding and efficacy of sustainable blockchain projects. By analyzing the global financial system and emerging trends, the chapter highlights the potential and challenges of integrating green crypto into mainstream markets. Check out their ReFi research and learn how you can support the mission.

New Research from the Bonding Curve Research Group
BlockScience is engaged in a research collaboration with the Bonding Curve Research Group, who have made recent contributions to the field of Token Engineering. Explore their latest article on bonding curve parameterization and adaptive token economies, including a new Gitbook library, bonding curve simulator, and model walkthrough summarizing of over a year of bonding curve research.

TheOSSci Open Source Science Initiative: Economic Interest GroupJoin our Chief Engineer, Michael Zargham and Philipp Koellinger of DeSci Foundation and DeSci Labs, along with a panel of experts in economics and open science, for the inaugural event of the The Open Source Science Initiative. At this event the panel discussed distributed systems, satellite data analysis, and reproducibility challenges. They emphasized the need for standardized technical training and the development of tools to ensure replicable research. The talk highlighted data supply chains, attention economy, and proposals for fostering open-source practices in academia.

KOI-Pond: The creation of a synthetic deme
Knowledge networks that can outcompete large institutions, particularly in fostering innovation, emerge through collaborative coordination among peers. We are grateful to Elinor Rennie and the Metagov Project for their pivotal role in bridging today’s technological capabilities with future collaborative methods.

At BlockScience, we are exploring how groups’ knowledge, practices, rules, and norms can be activated through LLMs and other technologies. This initiative involves creating synthetic demes that mimic their natural counterparts, in partnership with the Metagov Project. Our goal is to advance the integration of advanced governance systems into real-world applications.

🙏 Thank for your very valuable attention and stay tuned for the next quarterly newsletter… From our team to yours… Have a wonderful summer! 🌞💙

ABOUT BLOCKSCIENCE
BlockScience® is a complex systems engineering, R&D, and analytics firm. By integrating ethnography, applied mathematics, and computational science, we analyze and design safe and resilient socio-technical systems. With deep expertise in Market Design, Distributed Systems, and AI, we provide engineering, design, and analytics services to a wide range of clients including for-profit, non-profit, academic, and government organizations.

🌐 Website | 🐦 Twitter | 📚 Medium | 👻 Blog | 🎥 YouTube | 👥 Linkedin

Quarterly Newsletter: Biomimetic Economic Systems, Spatial Canvases & Brazilian Bike-Packing was originally published in BlockScience on Medium, where people are continuing the conversation by highlighting and responding to this story.

BlockScience’s work often entails adapting and applying institutional analysis frameworks to better understand the ‘functional architecture’ of our clients projects, in order to provide contextually appropriate systems engineering solutions for the environments they create and exist within. In this article we turn the microscope around, using those frameworks to map the functional architecture of BlockScience as a cybernetic organization. By sharing some of the internal sensemaking that we have used to better understand the interdependent flows of our own org, we aim to propagate these modes of organizational awareness to enable experiments with more emergent and adaptive institutional forms.

Understanding the BlockScience Organization

BlockScience is an engineering and R&D firm with a focus on the design and analysis of complex systems, as well as the cybernetic steering processes that govern them over time. Our name is derived from how we use Blocks of Science to manage complexity with Systems Engineering, for example in Model-Based Institutional Design.

The organization is functionally structured as a semi-autonomous workers cooperative, with similarities to sociocracy in terms of empowering strategic and tactical autonomy between sub-units. As a remote-first collection of professional engineers and researchers, integrity and autonomy are core values upon which we build trust and engender independent thought, initiative, and innovation among our team members.

BlockScience’s sub-units center around the functions that the organization performs, rather than as hierarchical management systems. Individuals can participate in fulfilling multiple functions (or sub-functions) in overlapping roles. Each group is guided by a council of peer members from all stakeholder groups, who meet periodically to discuss challenges, navigate trade-offs, identify opportunities for improvement, and facilitate overall coordination between organizational functions and stakeholder groups.

Mapping Our Functional Architecture

Our institutional analysis toolkit includes applying Stafford Beer’s systems thinking heuristic “the purpose of a system is what it does” (POSIWID) to identify the primary functions that an organization fulfills. Through our mapping process, we discovered that our organizational architecture is currently defined by five multidisciplinary functional groups, which we will explore below.

BlockScience’s primary functions include the following:

• Produce: Research & development across three interrelated domains: Technology (enabling infrastructure), Economics (market and mechanism design), and Governance (computer-aided in particular).
• Apply: Application of R&D in professional services projects with aligned research collaborators, grant programs, and clients.
• Communicate: Disseminating information about tooling, experiments, research, and lessons learned from applications to wider audiences.
• Execute: Execution of internal and external contracts, policy-making, operational infrastructure production and maintenance, administrative authority.
• Govern: Collective organizational steering in accordance with internal and external feedback from all other functions, with a focus on subsidiarity (and supersidiarity) where appropriate.

This depiction of our organizational architecture provides an overall view that emphasizes the “functional” (rather than hierarchical) structure and highlights the horizontal parallels between functions. It also illustrates the frequency of action, with primary functions being fastest, and overall organization governance being the slowest, by design. Team members can specialize in one function or contribute to several functions, creating value in various capacities according to their skills and interests.

For cross-functional communications and coordination, each group also operates a council consisting of peer members from within the group as well as representatives from the Operations Council or other stakeholder groups to ensure operational and strategic alignment with the overall organizational purpose, goals, and priorities. The Governance Council then consists of members from each of the other four functional groups.

While these diagrams are useful tools for describing the functional architecture of BlockScience, it is important to note they aim to describe, rather than prescribe, patterns of behavior that are already happening. Also, given that no single view can accurately represent all aspects of the organization (as George Box quipped, “All models are wrong, but some are useful”), it can be beneficial to view this diagram in concert with other models we’ve applied to our cybernetic organization, such as Viable Systems Models (VSM).

Exploring “Organizational Cybernetics” and VSM

The founder of Management Cybernetics, Stafford Beer, introduced the Viable Systems Model to represent the structures and functions necessary and sufficient for the long-term survival of a system in a changing environment. A simplified version of the five systems of Beer’s Viable System Model (VSM) can be seen below.

The VSM presents five types of organizational subsystems and the mechanisms of self-regulation applied by and to organizational settings:

• System 1: Productive Functions, or day-to-day production-oriented activities conducted by constituent parts of the organization. There may be multiple system 1 functions in an org.
• System 2: Coordinating Functions, which align the day-to-day activities of System 1 with each other and the collective purpose.
• System 3: Rules & Structures (such as software), which support the activities in System 1 and System 2.
• System 4: Strategic Functions that look both outward and inward to consider how internal systems might adapt to external changes.
• System 5: Governance Functions to align the overall organization, define or refine the goal(s), and resolve resource conflicts between the other systems.

As Kelsie Nabben states in Applying Stafford Beer’s Viable System Model to Decentralized Organizations: “The ‘hierarchy of functions’ that VSM distinguishes is different to a hierarchy of roles. Systems 1–5 are functions that anyone can participate in as long as they adhere to the stated processes or protocols for that function. To participate in multiple layers requires people to switch context between strategic and operational processes.”

One of the challenges faced by large organizations is that some issues are too complex for centralized decision-making groups. Management cybernetics recognizes that critical expertise and knowledge is often dispersed in an organization, which can be channeled into organizational intelligence (i.e. efficiency, or “doing the thing right”) or even organizational wisdom (i.e. effectiveness, or “doing the right thing”) by enabling functional autonomy through organizational viable systems modeling.

BlockScience as a Viable Systems Model

We often apply VSM as a tool to both design and diagnose challenges in organizational management processes for our clients. Mapping our own organization using this lens has already helped us identify functional improvements for incremental progress towards our shared mission.

The Viable Systems Model is useful for exploring how the different functions within our organization relate to one another, and how the overall organization can adapt to external changes in order to maintain resilience. As applied to BlockScience, the functions and councils fit into the five VSM subsystems as follows:

• System 1 — Primary Functions: Produce (R&D), Apply (in grant and client projects), and Communicate (to various audiences), carried out by each respective functional team.
• System 2 — Coordinating Functions: Councils exist within and between each primary function (Produce, Apply, Communicate) to coordinate them. Each primary research area (Gov, Econ, and Tech) also has its own coordinating council.
• System 3 — Rule Structures & Controls: Infrastructures consisting of people, processes and tools that are administered by the BlockScience Operations team to maintain internal cohesion. Provisioning and permissioning accounts, platforms and tooling provides the necessary “structure” of our digital office and collaboration spaces.
• System 4 — Strategic Functions: The Operations Council provides strategic functions, including external environmental scanning, forward planning, and policy-making, to ensure alignment with the overall company mission and values. The Operations Council consists of Ops team members who participate in each of the four functions (Execute, Produce, Apply, and Communicate). This ensures that each of the functions has representation in this council.
• System 5 — Governance Functions: The Governance Council is tasked with addressing policy or organizational changes, and is responsible for representing the interests of all stakeholders in the organization. This council consists of representatives from each of the other councils, allowing BlockScience to steer collectively as a cooperative organization.

The Art of Steering in Cybernetic Organizations

The root word of cybernetics is the Greek kybernetes which Ross Ashby described as “the art of steering”. As a system of dynamic governance in an organization, cybernetics provisions continuous feedback and control loops that allow for emergent manouvering and adaptation to changing conditions.

Our internal explorations around collective governance at BlockScience surfaced the need to focus less on voting and more on steering. Ultimately, the function of governance is to enable individuals to engage in the collective steering of an organization — to decide which choices to make when circumstances force trade-off decisions. Real governance often lives where real-world constraints limit our action space and we need to trade off mutually desirable things.

Across the organization, decisions must be made in a timely manner and thus by the actors (individuals or groups) with the appropriate vantage point. Tools and techniques like VSM and functional decomposition provide increased clarity about our interdependent decision-contexts and improve the feedback loops. These feedback loops enable mutual accountability and allow for the continued evolution of our organizations toward these collective steering processes.

The Evolution of a Cyb-Org

An important factor in all cybernetic systems is their evolution over time. Complex systems evolve with the changing behaviors of their participants — as we need them to! However, we must keep in mind that the relationship between the map and the territory is always changing.

The diagrams above present a snapshot in time of the functional architecture of BlockScience, but these functions and processes are not set in stone. One of the strengths of a viable systems approach is the emergent process of organizational learning that is enabled through feedback loops that enable the iteration and adaptation to changing people, processes, and the internal and external environment that we exist within.

In the six months since producing initial diagrams, BlockScience already identified the need for several new functional groupings, taking action to mobilize a Technical Project Management (TPM) pod to improve process standardization across projects, and a Grant Ops unit to identify sources of more aligned funding for BlockScience’s deeper research areas. This functional mapping process has already proven useful for the emergent growth of our organization.

Towards Greater Institutional Adaptivity

Fine-tuning our techniques for organizational analysis using VSM and cybernetics is not just a lot of fun (for us, at least!), it’s also an important institutional skill that we all need to develop in order to build towards a thriveable future. In an era of exponential change, industrial-era institutions can struggle to stay relevant in rapid-fire information environments. Cybernetics and systems thinking could give organizations the edge they need in order to remain agile.

As we continue to research, practice, and apply our expertise in model-based institutional design, ethical cybernetic organizational design isn’t something we can just talk about at BlockScience — it’s a muscle we must continue to train, to take us beyond just “doing the smart thing” all the way to “doing the right thing”. For any intrepid organizations out there, we invite you to join in this practice with us, and flex your institutional adaptivity. We hope you’ve enjoyed this exercise in cybernetic calisthenics!

Acknowledgments

This article was written by Jeff Emmett, with organizational content and edits produced by Dan Furfari and Michael Zargham, and additional copy and edits contributed by Renee Lee, Jessica Zartler, and Nick Hirannet.

About BlockScience

BlockScience® is a complex systems engineering, R&D, and analytics firm. By integrating ethnography, applied mathematics, and computational science, we analyze and design safe and resilient socio-technical systems. With deep expertise in Market Design, Distributed Systems, and AI, we provide engineering, design, and analytics services to a wide range of clients including for-profit, non-profit, academic, and government organizations.
🐦Twitter | 📚 Medium | 👻Blog | 🎥 YouTube | 👥Linkedin

BlockScience as a Cybernetic Organization (Cyb-Org) was originally published in BlockScience on Medium, where people are continuing the conversation by highlighting and responding to this story.

Kelsie Nabben
August, 2024

TLDR:

This post shares a new co-authored paper that shifts the emphasis from ‘data DAOs’ to networks of data intermediaries via data mesh architecture. I also discuss some of the thinking and context behind the research, and other milestones in the project, that were generously supported by a Filecoin Foundation developer grant (‘Improving Socio-technical Resilience in IPFS: Part 2’).

Brief Abstract of New Paper

A data mesh is about distributed data architecture at scale. Our paper argues that “Data Intermediary Networks”, composed of data products built on data mesh infrastructure, can foster co-operation and co-regulation among data product operators, data providers and data consumers. We argue that this leads to an increase in the variety of economically viable data products, along with an expansion of new markets within which those products can be networked together into meshes with the capacity to generate new kinds of value. We then outline the technical, operational, and governance architectures that underpin these networked data economies.

Read the full paper: Sisson, David; Nabben, Kelsie; Ben-Meir, Ilan; and Zargham, Michael. (2024).“Data Mesh Architecture: Interoperability, Co-Operation, and Co-Regulation” SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4880709.

According to Dietrich Ayala, long time IPFS project contributor, these concepts push on some of the intractable conundrums in the IPFS world. In particular, who “owns” content, and how to trace the data lifecycle of who publishes, pays, uses, and cares about it. These questions, that are straightforward in Web2, are incredibly difficult to answer in IPFS and other Web3 architectures. “This intermediary/mesh work helps us understand the shape of tools to meet user needs in p2p-available environments”, states Ayala.

Further Context…

I have been thinking about Decentralised Autonomous Organisations (DAOs) as data architecture for a while.

Part 1 of my research on improving resilience in IPFS culminated in a research paper and chapter of my PhD that looked at how social arrangements impact resilience in peer-to-peer networks (see: Nabben, K. (2022). “Decentralized Technology in Practice: Social and technical resilience in IPFS”).

Some of this began with writing about how DAOs may evolve existing governance architectures for data, such as DAOs as Data Trusts. I then moved to developing practical tools to help designers and developers think through social arrangements surrounding data management to improve resilience, such as the Decentralised Data Governance Pattern Library.

This most recent paper began as an attempt to analyse and operationalise the concept of ‘data DAOs’. Through desktop research and interviews, the output evolved beyond the rather nascent and mostly theoretical concept of data DAOs to explore the more applicable concept of ‘data meshes’. It was undertaken in conjunction with domain experts from BlockScience.

What guided the research was the question of ‘how is value derived from data?’. This led to an exploration of data as a product and data processing pipelines. A linchpin of this work was the use of an Actor Network Theory approach to identify data intermediaries as key actants in the creation of value in data economies. What’s interesting about this is that a focus on intermediaries seems counterintuitive in a domain of peer-to-peer architectures and ‘disintermediation’. Yet, these actors are critical in the production of value from data, and how they relate to one another determines whether a data ecosystem is centralised or decentralised.

Combined with the technical expertise of my colleagues, this led us to more deeply explore the data mesh architecture and develop the concept presented in the paper of ‘data intermediary networks’. I also plan to share a forthcoming academic paper on data intermediaries from an ethnographic case study perspective, which was presented on a panel for the ‘Platform Economies Research Network’ (PERN) conference in New York in April, 2024.

Enjoy the read and feel free to be in touch with questions, comments, and ideas.

Kelsie.

Data Intermediary Networks was originally published in BlockScience on Medium, where people are continuing the conversation by highlighting and responding to this story.

BlockScience recently published a research paper with the Windfall Ecology Centre, Possibilian, and SuperBenefit in collaboration with Durham Region and funding from Natural Resources Canada on the affordances of blockchain technology to improve building retrofit energy efficiency programs. A summary of the paper can be found below, with a link to the full version at the end of this article.

Executive Summary

“We are living in a time of unprecedented economic and environmental challenges. Having crossed six of nine planetary boundaries, we stand at the threshold of ecosystemic collapse and are already witnessing mass degradation of the natural environment and irreparable harm to people and the planet. We must drastically and rapidly re-align our institutions, technologies, economies, and human-made systems to the regeneration of our communities and natural ecosystems.”

Canada needs systemic shifts in energy efficiency retrofit programs in order to accelerate the national transition towards our net-zero emission targets by 2050. Homes and buildings account for nearly one-fifth of Canada’s carbon footprint, but at current retrofit rates it will take 142 years to transition residential buildings, and 71 years for commercial buildings. Yet our transition to a zero-carbon economy is needed within the next 26 years — preferably within the next decade, given the speed of the unfolding climate crisis.

The Windfall Protocol Research Group has completed a five-month study with funding from Natural Resources Canada (NRCan) to survey this landscape from a systems perspective and identify design considerations for a new layer of multi-stakeholder coordination and shared digital infrastructure. Partners including Windfall Ecology Centre, Possibilian, BlockScience, and SuperBenefit are researching and developing an open-source blockchain-based protocol to facilitate energy efficiency labelling and data sharing for residential and commercial buildings to accelerate retrofit adoption dubbed the Windfall Protocol. This paper outlines a high-level conceptual architecture and technical considerations, as well as a planned pilot in Durham Region.

The Windfall Protocol aims to address many of the gaps that exist between stakeholder groups in current building energy efficiency subsidy programs. For participants, it can reduce complexity of accessing information and retrofits, and offers new incentive pathways. For regulators and incentive providers, the protocol offers opportunities for smart and targeted incentives, improved data flows, and actionable net-zero strategies. This multi-stakeholder digital infrastructure could be leveraged for large-scale transformation of energy markets, open new avenues to finance energy efficiency retrofits, and supercharge the coordinative capacities and incentives needed to drive the volume of retrofits required for net-zero.

In addition to digital, technical, and economic innovation, the protocol will also require multi-stakeholder governance and representation in design choices and trade-off decisions with social, technical, economic, and environmental impacts. As part of these efforts, the Windfall Protocol Research Group is also convening a consortium of stakeholders to solicit feedback and engage in discussions on further development of the program and the protocol it aims to produce.

This paper informs the next phase of research and development for the Windfall Protocol Research Group in defining the Windfall Protocol Program — including a pilot implementation in the Durham municipality of Ontario, Canada. The goal is to create an open-source energy efficiency retrofit protocol with the steering capacities required to guide our systems on a more resilient and regenerative environmental and economic trajectory.

“The Windfall Protocol Research Group and stakeholder consortium offer a schelling point for coordination and leadership in this crucial mission. The group welcomes participation and collaboration to resource and further develop ideas explored in this report, and work together towards not just net-zero goals but net-positive ones, with the capacity for regeneration to serve seven generations and beyond.”

READ THE FULL PAPER HERE:

Litepaper | Windfall Protocol Docs

Discover more about the Windfall Protocol Research Group from its founder, Michael Lewkowitz: https://lewwwk.com/post8067/

Get engaged with the research! Questions and discussions about the lite paper, mechanism design, and collaboration opportunities are taking place in the SuperBenefit Discord.

Acknowledgments

This report was written by Jessica Zartler (BlockScience), Jeff Emmett (BlockScience), Michael Lewkowitz (Possibilian), and Brent Kopperson (Windfall Ecology Centre), with input and feedback from Michael Zargham (BlockScience), David Sisson (BlockScience), Kelsie Nabben (BlockScience), Rowan Yeoman (SuperBenefit), Rathermercurial.eth (SuperBenefit), and Ananth Nandakishore (SuperBenefit).

We would also like to thank all of the research and development partners involved in the production of this report and other key stakeholders in the Windfall Protocol community, without whom this research would not be possible.

About BlockScience

BlockScience® is a complex systems engineering, R&D, and analytics firm. By integrating ethnography, applied mathematics, and computational science, we analyze and design safe and resilient socio-technical systems. With deep expertise in Market Design, Distributed Systems, and AI, we provide engineering, design, and analytics services to a wide range of clients including for-profit, non-profit, academic, and government organizations.

🌐 Website | 🐦Twitter | 📚 Medium | 👻Blog | 🎥 YouTube | 👥Linkedin

The Windfall Protocol was originally published in BlockScience on Medium, where people are continuing the conversation by highlighting and responding to this story.