Humanity stands at a critical juncture, confronting global challenges of unprecedented complexity that necessitate a collective approach to decision-making, spanning from local communities to the global arena. The existing governance processes, however, show signs of strain under these demands. Many of these processes, steeped in historical legacies, now grapple with the need for evolution, while emergent methods still falter in achieving effective implementation at a broader scale. Some methodologies even amalgamate the drawbacks of both traditional and contemporary systems. This underscores an urgent need to reinvigorate the discourse on governance, moving beyond the conventional paradigms of international relations and political theory.

This paper introduces a novel hypothesis: envisioning governance as a versatile “Lego game”, composed of “bricks” — each representing distinct governance processes and systems. These bricks, varying in shape and color, symbolize the diverse elements that can be assembled to construct comprehensive governance frameworks or on the contrary help in deconstructing existing governance structures to scrutinize and refine them.

Employing a multidisciplinary approach grounded in "fuzzy logic" (Cintula et al., 2021; Zadeh, 1975), this paper offers a unique perspective, blending theoretical insights with practical experience. The author's involvement in key governance fields — Aleatorian democracy since 1998, deliberative democracy since 2003, and the Web3/decentralized governance ecosystem since 2017 — provides a rich, self-reflective ethnographic backdrop for this exploration. Rather than claiming a completely novel methodology, this paper is structured as a synthesis of experiences, tapping into research and the practical application of innovative governance formats. It also aims to serve as a foundation for the proactive design of high-quality governance architectures, taking into account not only the processes themselves but also their contextual applicability.

This paper is the inaugural installment in a series that will delve deeper into the practical implementation of the 'governance Lego' in various real-life political settings.

We cannot solve our problems with the same thinking we used when we created them. Albert Einstein

The tools we use for collective decision-making – also called governance – appear to have a major difficulty in coping with challenges of the 21st Century. The hypothesis I make in this paper is that the tools we use mainly date back to the 19th and 20th centuries and are not adapted to the 21st century’s society and its challenges. Not only do modern societies have profoundly evolved since then (buzzwords: Global population X10; digitalization; interconnectedness; Rising education level, etc.) but also Humanity is facing new challenges unknown to past societies, like Digitalization and Synthetic intelligence, Climate Change, growing inequalities at global level, loss of Biodiversity, polarization, technologies like genome editing able to change the very meaning of what it is to be Human, etc.

There is a clear lack of adequate institutions for decision-making and collaboration to effectively tackle those challenges. This situation translates into a growing gap between decision-making and the people those decisions are made for: citizens. An increasing part of the population feels excluded from the process or is objectively disenfranchised from it, which leads to even more blockages in the system. It generates a vicious circle of mistrust, harming the capacity of decision-makers, stakeholders, and populations to agree upon and then implement the needed solutions.

The focus of this paper is not to enter an in-depth analysis of why the tools are not relevant and/or effective. It is rather focused on proposing a framework for tools that could be up to the task. Therefore, I will approach the analysis of existing tools through a positive reformulation of what they should ensure to help solve our challenges. I will do this thanks to a set of criteria of quality. Before this, however, it is necessary to really understand what a decision-making process is.

During the classical democratic era in Athens, members of the Council of 500, or Boule – one of the main institutions of Athenian democracy – were chosen once a year from all citizens of Athens aged 30 or more by means of a random selection. Before they could perform their duties, the drawn “bouletai” had to pass a so-called “dokimasia” in front of the former Council. It was a test in which they were questioned about all sorts of topics (their family, values, etc.). Subsequently, they were confirmed or rejected by a hand vote. Once they were accepted, they had to serve for the year; their role was to prepare decisions to be put to discussion and voted in front of the Ekklesia, the citizens’ assembly (Hansen 1999).

In late 2020, the decentralized exchange Uniswap, which allows users to swap digital tokens, distributed its UNI token to its users (Kamarul 2020). This so-called “airdrop” was shortly after valued at more than €8.000,00. The method used to distribute the tokens was based on a snapshot of users (Ethereum addresses) that had interacted with the exchange. And each of these addresses received 400 digital “tokens”. To claim the tokens, users had to sign a transaction with their digital wallet. The tokens could then be traded or used in the governance system of the protocol to vote on decisions concerning the project. The vote is based on a weighted system (more tokens, more voices).

At first sight, these two examples couldn’t be more different from another. At second sight, they both illustrate a core feature of any Human Society: The distribution of resources through a process of governance. Lasswell (1935) called this politics and summarized it as: “who gets what, when, and how.” In this paper I will substitute an element of the list to make it: “who gets what, why, and how”. This change underscores the importance of understanding not just the distribution of resources, but also the underlying motivations and justifications that drive these decisions as we will see later.

The resources, the “what” can be called items and can be divided into three categories, following Elster (1987, 108):

Lots: These are tangible or intangible assets distributed through governance. Examples include material assets like a computer, or immaterial assets like a ticket to an event, or digital assets like the UNI token previously mentioned. Functions: Functions encompass roles or responsibilities that can be either non-political (like specific jobs or tasks) or political. Political functions typically fall into six categories: Legislative: Concern the rule-making processes. A perfect example is a parliamentarian. Executive: Focuses on the enforcement of these rules. Typically civil servants. Judiciary: Ensures the conformity of rules with overarching laws or constitutions and enforces sanctions for rule violations. A judge would incarnate this kind of function. Consultative/Advisory: Provides advice or suggestions. This could be the role of citizens in Citizens’ Assemblies for example. Elective: Corresponds to the function of choosing other function holders. For example, the board of a company can choose who is going to be the new Chief Executive officer. Decisions that are to be understood as the selection of a course of action (turning right or left for example). This category is too vast to create a clear typology. That being said, if we focus on core decisions on governance (decisions on how to decide), we can pin down the following major categories: Organizational decisions: creation and dissolution, membership (inclusion / exclusion, rights, and duties), rulemaking (constitutional process for metarule and legislative for normal rules), diplomatic (relations to other organizations), budgetary decisions: decide on incomes and expenses, functional decisions (more granular kind of decision within the limits of the organizational and budgetary boundaries. We can call them projects or programs).

Using this distinction, the selection for the Boule can be described as a consultative function as the Boule prepared the laws for the Ekklesia (see Blackwell 2003, Hansen 1999 or Headlam-Morley 1891). The Ekklesia had the legislative function. The UNI airdrop can be described as the distribution of a Lot, giving the right to participate in the legislative function of the UNI project. Worthwhile noting and maybe less than a coincidence, both required a token: In the first case it was the name of the citizen put on a piece of bronze and used in the Kleroteria (random selection machine) in the second case a digital ERC20 Token, a standard for digital tokens on the Ethereum Chain.

A selection procedure can be described as a process by which a set of items is reduced to one: “moving from many options to just one” (Ullmann-Margalit 1977; Elster 1987). In the Boule example, the set was the totality of the male Athenian citizens over thirty. The reduction was performed by the combination of a random selection, an examination, and a vote. The procedure was repeated for each of the 500 Bouleutai. In the case of UNI the set was all Ethereum addresses and the reduction was performed on the basis of a single criteria: The interaction with the protocol.

There have been many attempts at creating a typology of procedures and it's still a field in discussion[1]. I have in the past created a typology differentiating the following 4 types which I will use in this paper: Vote or Psephos, market or Agora, random selection or Kleros and examination or Dokimasia (Vergne 2013).

How to distinguish these four procedures from another:

• The vote uses the aggregation of individual opinions as a means of reducing the set of options. In a vote, preferences are translated into suffrages that are added with the help of an “algorithm” (simple majority, proportional representation, absolute or relative majority, consensus, quadratic voting, etc.). This results in a decision.

• In a market procedure, individual preferences are matched (reduced) using a price which is the balance between preferences (offer and demand).

• In an examination, the reduction is done by checking if a list of prefixed criteria are met. The most known of these criteria are rotation, birth (or nationality, inheritance), needs and merits, or time (a queue for example). Since the relevant criteria are defined by the actors themselves, and are combined, it is impossible to conduct a systematic enumeration here.

• Random selection is different from the three previously described methods in that the transformation of the individual preferences into a collective decision bypasses the will of the actors: there is a moment of “uncertainty”. Random selection is a chance device (Alford 1958, 2) and reflects the “intentional choice to make the decision by a non-intentional mechanism” (Elster 1987, 108). The reduction is therefore based on the use of an aleatory moment.

[1] The exact number of types of procedures is still being discussed. Stone (2009, 232) distinguishes seven methods: “Lots”, “Vote”, “co-optation”, “auction”, “strict rotation”, “temporality” and “monopolized allocation” (which he divides into different groups: distribution on the basis of certain “merit”, “suitability” or “needs”). Elster (1987, 128 sq) distinguishes six types of procedures: “random selection”, “equal physical division”, “allocation” (three types: allocation based on “needs”, “productivity” or “merit”), “market”, “queue” and “status”. Kornhauser and Sager (1988, 483) suggest a division in four types: “vote”, “allocation” (on the ground of “merit” or “need”), “market” and “random selection”. Saunders (2008, 361) distinguishes between three methods: “selection”, “random selection” and “auction”.

When Athenians selected their lawmakers by lot, they did not consider that they were chosen by the gods (Manin 1997). On the contrary, when they went to the Aruspice to interpret their future by reading the guts of a dead animal, they did believe that gods were involved (Bromberger & al. 1987).

This distinction is important as any decision-making has a “material” and “intellectual” dimension. The Intellectual dimension is both the way people justify the procedure and the way they interpret it:

Justifications: When making a decision people refer to “reasons why” they do it. For example, when selecting through an election they refer to transparency or competence. When recurring to random selection they put in front reasons like equality of chance or protection against corruption. The market is often justified as being efficient or scalable or able to exist without a central coordination mechanism. An examination is justified by reasons like need or rationality. We will see later that justification is the key to understanding governance systems.

Interpretation: There seem to be 3 main regimes of interpretation: The first one is finalist meaning that the output is seen as the consequence of the will of a superior entity (god, the gods, destiny, etc.) the second one is deterministic meaning that the output is seen as the result of a causal chain that can be traced. The last one is probabilistic meaning that the output is seen as being based on a statistical probability of the event occurring given the input conditions.

This dimension of decision-making may seem very logical and uncontroversial, but it has 1 major consequence: Decision-making is part of a narrative

None of the 4 decision-making procedures presented before have a “natural” or “intrinsic” property. If you claim to want to use a market-based decision-making because it is “transparent” but you let insider knowledge bias the mechanism, you are creating a non-transparent mechanism. If you choose your rulers based on their merit of being born in a cast or family (kings and queens) you are recurring to random selection, as birth is random. But you may well interpret it as the will of a superior force, in which case this process is for you based on merit.

Having this in mind allows you to open a large spectrum of design possibilities as you can play with the narrative and the decision-making tool proactively.

When looking at real-life governance, this typology reaches its limits as no real-world process is a clean-cut “vote” or “market”. Any governance is embedded in a more general process. One way of approaching this is to consider “procedural blocks” as a series of procedural steps resulting in a decision. Taking the example of the Boule the block would start with exanimation (being a man over 30), continue with a random selection, and then finish with an examination (in front of outgoing bouleutes). This approach will allow us to imagine decision legos in a much more realistic way.

This part of the paper has shown that decision-making processes have no intrinsic characteristics. It doesn’t mean that they are all equals. There may be “bad” processes and “good” processes. As these terms are highly normative, I prefer to approach this question through the lens of the quality of the procedural blocks.

Quality refers to the fact that something is judged satisfactory by the people interacting with it, regarding a series of criteria. In a hotel, quality could refer to the cleanliness of the room, the responsiveness of the personnel, the noise, etc. In this part of the paper, I focus on the criteria for a high-quality governance process. It is based on previous publications and inspired by many existing frameworks (Vergne 2011; Vergne 2014). As stated before, the unit of account is the procedural block.

A high-quality process should be capable of engaging everyone in the process as inclusion is the key to legitimacy but also to a higher cognitive quality of the decisions taken (Landemore 2014; Surowiecki 2004). So, inclusion is meaningful in terms of input and output. Inclusion has three dimensions that can be complementary or additional.

Quantitative inclusion

This means that as many people as possible are included in the process. Such an inclusion ensures a direct ownership of the process and its results. A good example of a potentially very quantitatively inclusive process are general elections coupled with universal suffrage as any citizen may participate.

Qualitative inclusion

This kind of inclusion means that the most diverse set of opinions and viewpoints are included in the process. This can for example be achieved by sortition (random selection) of participants or by carefully mapping and including representatives of very different positions.

Incentives

Incentives are a set of leverages that motivate participation. The higher the incentives, the stronger the probability of having both a large quantitative and qualitative inclusion. Incentives are commonly divided into 4 groups: Social, financial/material, political, and cognitive (Mazeaud and Talpin 2010).

Social incentives are top-down (group pressure and norms that “force” participation) or intrinsic (wish to be part of a Human group, sociability, joy). Social incentives relate to the very nature of Human beings as social beings (Bregman 2020).

Financial incentives are straightforward as they motivate participation against payment. This form of incentive is the backbone of industrial societies. It’s also a key feature of deliberative processes as participants are often rewarded for their participation based on the old tradition of the Greek obola. Financial incentives are of course the central feature of blockchain ecosystems. As Buterin (2018) puts it, the goal is: “to reduce social trust assumptions by creating systems where we introduce explicit economic incentives for good behavior and economic penalties for bad behavior.” Material incentives are another form of direct incentives. For example, the invitation to gather at a single place face-to-face all expenses paid is a very strong incentive that largely enhances inclusion.

Political incentives connect to the wish of Human beings to be part of the collective decision-making process, to influence it and/or to control it. They are the backbone of any decision-making process.

Cognitive incentives are based on the wish to learn and experience. It’s a very powerful motivation in any collective endeavor even if it is often ignored, downplayed or even mocked in many societal contexts.

A high-quality process can be measured against the argumentative process it goes through. Following Habermas, an ideal situation could be described as one in which the final consensus or state of a discussion is based on the force of the best arguments and not on power situations. A deliberative process relies on the following steps that are partially sequential and partly parallel or in loops:

Information: In this phase, the goal is to gather information from participants or from outside. It can be opinions, facts, or expertise.

Debate: The second core component of good deliberation consists of an exchange of diverse and/or opposed views, facts, and opinions on the subject matter. The goal of a debate is to confront different perspectives. It’s the key element of the judicial process. Having a debate is central to “building dissensus” or at least exploring it. It allows us to open the discussion and make sure that no key elements are overlooked. The debate can arise naturally or can be provoked through a facilitated process to overcome social complacency.

Dialogue: In this phase of deliberation the goal is to go from collected intelligence to collective intelligence in the sense that the information and the debate are melted into an exchange of arguments and the process of finding common ground. Common ground can be an agreement or an agreement on a disagreement.

Aggregation: The focus of this phase is to end the process. Aggregation can be made through one of the four decision mechanisms evoked above. It may be a consensus, a majority vote, a market process, an aleatorian process, or a mix (onchain vote with tokens which mixes market and vote for example).

Results: Once the cycle is over, the deliberation produces an outcome which is a result. This can be the final result or a result opening a new cycle of deliberation.

All steps can be iterative: A debate often gives new information, a dialogue opens debates, aggregation may result in the need to open a new debate, etc.

It is worth noting that this criterion may seem irrelevant to market mechanisms. This view ignores the fact that deliberation is taking place “around” the selection procedure, as part of a procedural block. The agreement on a price is the result of the deliberation of parties on their perceived quality of the product, or good or else.

A highly qualitative process should be relevant. It means that it should be in line with what participants want to achieve. This ensures that the process fits the purpose and is neither over-engineered nor under-engineered. It also makes sure that resources needed are evaluated, so the decision-making is efficient. There is no need to have the full participation of everyone to decide the color of a bank or the background of a website (you know those meetings with the front-end team and the management).

A relevant process is also enforceable: It has an impact on the community and has consequences.

Finally, a highly qualitative process demands fairness. The first subcriteria of fairness is reflexivity meaning that participants should be able to talk about the process itself and change it if needed (Dryzek et al. 2017).

Transparency is the second key characteristic of a fair process as it levels out power asymmetries.

The final sub-criteria of fairness considers the sustainability of the decision-making process understood as societal, ecological, and economic sustainability. Sustainability is also connected to its temporal dimension (how does it impacts the 7th generation) and its legacy (what will stay).

In this section, we have gained clarity on the quality of a procedural block of decision. We have gone from a decision-making mechanism to a decision-making procedure, to a decision-making procedural block.

This architecture is very useful, but it misses a broader perspective because decision processes do not exist in a theoretical bubble, they are part of a governance system or ecosystem. Therefore, I now turn to a new typology.

The classical Aristotelian typology of governments differentiates between pure and mixed systems and between two dimensions: Who governs and for the benefit of whom (Hansen 1999). This is a highly effective way of classifying governance systems which can also be tagged as polities following the old definition of a polity being a political system. I will use the typology in a fuzzy inductive-deductive empirical-theoretical typology of ideal types of governance systems. By this, I mean a typology that mixes some real-world use cases with more theoretical ones, and “seems right” because they are ideal types, and we can understand what they mean.

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To illustrate the typology let’s take a couple of examples. First, if we look at one person governing it its own interest. The despot as ideal type seems quite relevant. If we now turn to some people governing for the benefit of everyone – or at least the immense majority – based on their birth. This rings like a good old aristocratic system. If that same limited group of people governs for the benefit of a majority based on merit or competence, we have a Technocracy. Replace merit by a vote (the few being elected and re-elected) and we get the ideal type of representative democracy. A government in which some randomly elected citizens govern to the benefit of all is an aleatorian democracy.

This paper is not the place to dive into an exhaustive panorama and assessment of all governance models presented above as it aims at setting the primitive. A series of following case studies will dive into specific polities and/or procedural blocks, their limitations in terms of governance, and proposals to improve them. This section focuses on shedding light on the most current/existing models with the perspective of the criteria of quality. And without any delay, it can be said that they perform poorly.

If we start with the most spread models, we find many nation-states (around 90) governed as some kind of autocracy, plutocracy, or nepotism. Around 90 countries of the world live under a form of hybrid regime or flawed democracy or technocracy, with a reduced group of people governing in the name of all (although not really for the benefit of all) (Democracy Index 2022). According to the Democracy Index, only 21 countries qualify as full democracies. Even there, the extent to which government is “for all” is questioned. If we look at governance models outside nation-states we find mostly corporations or hierarchic organizations like churches in which the majority of the people serve a reduced group governing in its own interest (shareholders).

These forms are of poor quality because they are not inclusive, they are not deliberative, and surely not fair. They are in many cases relevant because they can be efficient and can scale. Nation-states have shown how well they managed the industrial revolution. However, they are not sustainable as they are not in a capacity to integrate long-term thinking and are based on an extractive model and not a regenerative one.

Luckily, many people have been working on improving this situation and finding, testing, and improving new bricks of decision-making, new procedural blocks, that would live up to a high quality of decision-making: Nice, colorful, decision legos.

We have delved into the study of decision-making, its components, some possible criteria of quality, and the reason why many existing constructions are of poor quality. But there is hope, as deconstructing a Lego into its pieces is the best starting point to to build new models. And with those bricks come millions of combinations, billions of possibilities to play with decision legos to create inclusive, fair, relevant, and deliberative decision-making processes. It is worth noting that this approach has the immense advantage of being fractal: You can scale the Lego bricks up or down at any level and they look the same. A decision within a team of three or a crowd of millions can be dealt with through a comparable process.

This paper aims at proposing a framework, a primitive. It is not the place to delve into the details of designing concrete models of governance or passing them in review. Also, many questions on piloting, and implementing new sets of legos remain unaddressed. It may be that some bricks can not be plugged on some others. That too many colors make it difficult to read the ensemble.

In an upcoming and ongoing series of use cases, I will focus on precise ecosystems, polities, or procedural blocks. Particularly, I will start exploring such ecosystems and combinations that seem to be the most promising to achieve better decision-making in the 21st century quickly and that have been piloted and developed with an expressive goal to counteract the shortcomings of the most widely spread polities. These are on one side bricks leveraging deliberation and sortition and on the other side bricks activating and mobilizing Web3 principles and infrastructure.

Humanity is mature to engage in new ways of making collective decisions that are aligned with planetary boundaries and respect our very nature as social animals. Engaging in the reflection, prototyping, and testing of decision-making legos seems to be a productive path to let a new renaissance flourish.