Artificial Utopia: Simulation and Intelligent Agents for a Democratised Future
Yannick Oswald
1 Institute of Sustainability and Geography, University of Lausanne
March, 2025
Abstract
Prevailing top-down systems in politics and economics struggle to keep pace with the pressing challenges of the 21 [st] century, such as climate change, social inequality and conflict. Bottom-up
democratisation and participatory approaches in politics and economics are increasingly seen as promising alternatives to confront and overcome these issues, often with ’utopian’ overtones, as proponents believe they may dramatically reshape political, social and ecological futures for the better and in contrast to contemporary authoritarian tendencies across various countries.
People are meant to become active agents of change with a much deeper engagement in decisions about the purpose of political activities and the allocation and distribution of resources
. 2 From Social Choice to Artificial Utopia - a research agenda
Traditional mathematical models of democratic processes include social choice theory and public choice theory. While these theories uncovered various im
portant features of voting systems like Arrow’s impossibility theorems or the Condorcet paradox,
they rely on idealised and axiomatic assumptions about political systems. Social and public choice theory often employ equilibrium-oriented models. Similarly, collective behavior models in eco
nomics, such as studies in opinion formation on networks or studies of ’herd’ behavior, focus on mathematically tractable dynamics that converge to specific equilibria. But equilibrium models naturally limit the scope of analysis because often they either do not specify explicit dynamic processes, which is critical when understanding how collec
tive decisions unfold, or, if they do, assumptions about the behavior of agents are purposefully so restrictive that the dynamic process exhibits an analytically tractable character. This is why they fail to grasp the complex, ’messy’ and highly dynamic nature of real-world demo
cratic processes and, therefore, are not well-suited to imagine new forms of political and economic democracy that go substantially beyond the status quo. Because precisely when testing new ideas,
it is important to scope a wide range of behavioural outcomes, which can arise from fluctuations and strong heterogeneity of people’s behavior and values. In contrast, computational simulations are increasingly employed to explore explicit alternatives to existing political and economic systems. For instance, some computational work challenges the notion of ’capitalist realism’ - the assertion that most political and economic scientific models operate within a fixed capitalist logic. This work intends to test radical changes to politics and economy, which lie outside of a capitalist economic logic,
but does so in computational simulations, rather than in real-world experiments.
The idea is that then one does so in an environment with substantially less risk of negative consequences. In a related study, Gerdes et al. simulate a hypothetical
’utopian’ society in which small-scale communities plan their economic production and trade with other communities in a ’needs-based’ ex-ante framework, independent of conventional economic
markets. Earlier, Almudi et al. studied distinct ideologies competing for different kinds of future utopias. However, from these studies, few implications for policy are deduced. Some work approaches novel democratic systems in more specific ways using agent-based simulation and also linking to existing structures. Carpentras, Hänggli Fricker, and Helbing study participatory budgeting designs and find that lessons from ’collective intelligence research’ can aid particularly in the inclusion of minority groups when solving policy problems. Another recent work investigates consensus formation in citizen assemblies in a polarized society via multi-agent models. The advantage of computational approaches is therefore twofold: First, they allow for the synthesis and testing of hypotheses within artificial settings, enabling simulations of political and economic dynamics to test a multitude of hypotheses that may be impractical or too slow to develop and to evaluate in real-world contexts. And moreover, they prove well-suited for theorizing
why, that is under what conditions, democratisation efforts succeed or fail. What is still missing from the computation-oriented discourse is however at least five-fold: (i) there is no integrated theoretical framework guiding research efforts, (ii) there is little clarity on what kind of simulation approaches to employ for what kind of research questions; what are the appro
priate domains of validity for different approaches?, (iii) there is little engagement with, or learn
ing from, existing or past egalitarian societies, (iv) the transition dynamics from a status quo to a
’utopia’ society are underexplored and (v) there is little clarity on what challenges emerge during unorthodox democratisation efforts and how to overcome them. I propose that research focuses on closing these knowledge gaps, and I suggest a partial agenda. The principal idea is to follow diverse proposals of democratisation and test their claims. This agenda might draw inspiration from alternative political, economic and social modes that so far are stud
ied predominantly outside of the computational sciences. For example, there is a wealth of ideas on how societies can manage to be economically and politically democratic and egalitarian in anthro
pological research, but to date this has in
spired few simulation efforts. Most anthropologically inspired simulation-based research focuses on explaining observed behaviour in existing tribal and marginalised societies, but not so much on transferring ideas for the purpose of reinventing society and testing them at scale.
Nonetheless, on top of that, I suggest aiming for generating as much theory
empiry synergy as possible and therefore also advocate for pragmatism when choosing research
topics. Following this pragmatic line of thinking, two prominent ideas that stand out among democrati
sation efforts are citizen assemblies and democratic firms, the latter which includes worker coop
eratives. This is because they are not only theoretical in nature but have been successfully imple
mented several times across countries and cultures. The related literature in case studies is rela
tively rich. Furthermore, several activist movements and organi
sations advocate for them. Citizen assemblies, for example, are a key demand by the climate move
ment Extinction Rebellion. Democratic firms exist world-wide. While still a
minority-type of firm, some, like the Spanish Mondragon Corporation, are large multi-national
enterprises. This ambiguous status, as a partially existing ’minority in stitution’, and as a radical alternative to predominating modes of political and economic decision making, makes citizen assemblies and democratic firms uniquely suited to be studied in the context of ’Artificial Utopia’. Both allow for empirical insights based on existing real-world case-studies and consequently for comprehension of their character at the individual and collective level, while also still being largely obscure from a computational and theoretical perspective. Moreover, both are sufficiently generic in their flat organisational structure and internal deliberation processes that lessons learned might be generalized to other democratisation efforts, including developments in digital and augmented democracy or democratic planning. This way,
theories of ’utopian’ democratisation can be developed alongside empirical foundations. Indeed,
while there are some past studies of democratic firms in economics, similarly to social choice the
ory, they employed idealised equilibrium models largely without grounding their assumptions in empirical knowledge and without representing complex temporal dynam
ics. Instead, the minimal requirement for an Artificial Utopia - which I define as a useful simulation framework for studying these institutions in a societal context - is to include those features. One option then to study ’utopia’ via simulation, but far from being limited to, is to focus on the following exemplary set-up: I suggest a citizen assembly representation with different levels of possible decision-making capability. In reality, most currently existing assemblies only consult governance procedures but are not executive organs – in simulations that could change. More
over, such a framework can include the distinct stages of a citizen assembly with (a) the selection procedure of citizens, (b) the selection and presentation of political issues and (c) the decision making process. A similar distinction of stages has been made in Barrett et al. but it has only been partially simulated. For modelling step (a), it is possible to employ known algorithms to select ’artificial citizens’ from a population.
Crucially, the economy should be understood and represented as a complex evolving system. Therefore I also suggest to apply this understanding to democratic firms specifically. They could be integrated into an evolu
tionary model of the economy where they must compete with conventional hierarchical firms in order to change the overall economic paradigm. For this purpose, a Schumpeterian and Keynesian
agent-based model is possibly ideal. The Schumpeterian school empha
sizes evolutionary bottom-up dynamics with firms and entrepreneurs as creative (and destructive)
forces. The Keynesian school, complementarily, emphasizes gov
ernment intervention and monetary policy, which can incentivize or even strongly support certain transition processes. While specifics should belong to defined research efforts, here I outline representation options. In Figure 1, for example, I show how both citizen assemblies and democratic firms could come together in a holistic understanding of alternative societies. Going beyond that, in the following
section, I will discuss in more detail what kind of simulation approaches could enable specific aspects of Artificial Utopia to become fruitful research ground. Figure 1: Artificial Utopia concept This figure presents a conceptual high-level presentation of a potential Artificial Utopia. In this specific instance, citizen assemblies are implemented as a governance organ, while democratic firms compete against conventional firms in a market-economy.
Importantly, from a computational perspective, they may also open up new possibilities for algorithmic game theory to probe complex interactions for specific equilibria or inherent instability, especially when con
sidering repeated games over many time periods, which then also opens up dynamic deliberation processes to game-theoretical analysis. 3.3 Agent-based modelling
While game theory often focuses on few agents or bilateral interactions, agent-based modelling
(ABM) is a general purpose bottom-up simulation approach that is able to represent large num
bers of people and which is increasingly applied in economics and politics. Among other things, it has been applied to consensus finding,
negotiation and spread of opinions. ABM is very well-suited if interactions and social networks between agents play a role in shaping aggregate emergent outcomes despite agents being ’locally informed’ decision-makers. Yet, there are few studies that tackle concrete questions around socio-political and economic-political institutions, particularly when confronting the possi
bility of reimagining the status quo and engaging in the search for alternatives. Common critiques to agent-based modelling include that it is difficult to verify them empirically and that they are computationally expensive. Improving empirical verification and validation how ever has become a primary research focus in ABM. Overall, the potential for ABM to explore utopian economics and politics seems vast because one can build ’tan gible’ model worlds, and depending on the simulation interface observe them evolve in real-time
(for example in Netlogo). 3.4 Reinforcement learning
A key limitation of traditional agent-based modelling (ABM) is its reliance on simple, stochastic rules to represent human decision-making, such as fixed savings rates in an economic model for
example. While these heuristics mirror theoretical assumptions, they often fail to capture the adap
tive, pattern-analyzing nature of human behavior. Reinforcement learning (RL) is an approach suited to overcoming this limitation by making agents learn behaviors through in
teraction with their environment. Unlike pre-programmed rules, RL specifies goals and objective functions that agents aim to maximize, leading to emergent, goal-driven behaviors. Deep reinforcement learning (Deep RL), which combines RL with neural networks, has achieved remarkable success in domains like chess, Go, and video games and training large language mod
els. Moreover, RL mirrors mechanisms of human learning in
neuroscience. Applied to ABM, Deep RL modelled cognitive mechanisms in classic frameworks like Sugarscape and Schelling’s segregation model. Deep RL has been further applied to public good problems and taxation models, where government agents optimize policies while workers optimize income
strategies. These innovations may enhance ABM’s ability to simulate complex societal dynamics realistically.
Of course, there remain substantial methodological challenges in applying reinforcement learning to complex multi-agent settings such as understanding the emer
gent dynamics of a non-stationary system with many learning and adaptive agents which is particularly important for agent alignment and desired aggregate system properties such as sustainability, for example. 3.5 Large Language Models (LLMs)
Large Language Models are probably by far the most prominent technology in recent years. Since the introduction of ChatGPT in 2022, they have sparked both optimism and alarm on their methods and capabilities. In the case of the computational simulation of democratisation
processes, they might prove very useful. LLMs excel in natural language understanding and generation, enabling them to simulate linguis
tically sophisticated agents. This capability can also enhance agent-based models (ABMs) by in
troducing conversational dynamics between agents and perhaps improve specifically political and economic investigations where language plays an important role. Indeed, LLMs
already can simulate inter-agent political dynamics in settings such as the US senate or are used in ’augmented democracy’ for providing ’expert peers’ to citizens that sup
posedly enable them to make more informed decisions in democratic processes. LLMs also are increasingly used to study the emergence of cooperation and sustainable or unsus
tainable behavior. For example, they have been applied to study climate negotia
tions, which could be transferable to citizen assemblies or a democratic firm
context. By incorporating LLMs, ABMs may achieve more realistic simulations of democratic pro
cesses but come at a high computational expense, specifically during the machine-training process. Furthermore, there are many methodological limitations and risks associated with large language
models, but our goal here primarily is to scope what they could contribute to scientific discovery in
Artificial Utopias. For a comprehensive assessment of risks emerging from sophisticated agents,
like large language models, the reader might refer to Hammond et al.. Directly relevant to this perspective here is, however, the issue of value alignment in LLMs. Because LLMs are trained on text data from specific contemporary cultures, it is questionable how well LLM agents can represent change in values and culture. More specifically,
it is an unsolved problem how, and if at all, LLMs can represent a value and culture shift that a citizen assembly or cooperative firm might cause. Are they capable of representing the change of values of an individual person as well as social transformation?
And along which of their properties would we measure such transformation and how does that map to the analysis of humans? 3.6 System Dynamics
System Dynamics (SD) is a numerical equation-based approach. It is appropriate for analysing systems that can be divided into stocks and flows of quantities and feedbacks between those quan
tities. In our context, SD constitutes a complementary aggregate-level approach to the other more
individual-focused approaches. Indeed, within political science or economics, SD so far has mostly been applied to tackle ques
tions in macro-scale systems like international relations, global political dynamics
and macro-economics. SD can also be coupled with micro- and meso
level approaches such as ABM for a holistic system analysis. We explore below how SD could contribute to the study of utopias and citizen assemblies and democratic firms in particular. Figure 2 summarises the introduced simulation approaches and systematises them according to
function, approach and scale. In terms of function, reinforcement learning and LLMS are focused on more realistic representations of human cognition and behavior, ABMs and game theory on in
teractions and group dynamics, and system dynamics on emergent effects and institutions. Figure 2: Simulation approaches classification:
This figure presents a systematization of distinct modelling approaches according to function and scale. Although these features characterise certain domains of validity, none of the domains are absolute. Approaches like Large Language Models are considered micro-level in this context, because we consider them for the application of enhancing models of human cognition in agent-models. In other research domains, large language models may have entirely different purposes.
This is similar for the other approaches, too. 3.7 Mapping Potential Issues in Citizen Assemblies and Democratic Firms to
Simulation Approaches
In this section, I map the simulation approaches to challenges identified arising in citizen assem
blies and democratic firms and which seem suitable to be theorized with the help of computational
simulations. In doing so, I outline a set of pathways for advancing the study of Artificial Utopias. I
focus on challenges arising in those systems because, despite the fact that citizen assemblies and democratic firms are found to work quite well in many contexts, it is particularly of policy rele
vance to enhance our understanding of what might go wrong in either of the two cases and within democratisation efforts at large. This way, institutional designs can be made more robust and re
silient against anti-democratic forces and systemic shocks. 3.7.1 Identified challenges
Citizen assemblies are complex multi-stage procedures with challenges at each stage. The first stage includes defining what issue a citizen assembly negotiates and what the desired outcome
is, as well as the selection of citizens. This stage can be subject to manipulation through influen
tial stakeholders and be subject to selection biases, for example, self-selection bias in participants
. The next stage, in which participants learn and deliberate about the policy issue of interest, might not always be effective or inclusive. For instance, it has been argued that aiming at consensus for a policy decision could be detrimental, given diverse underlying needs of the population and given that some groups might be less prone to defending their position than others. Even if consensus is achieved, it can also hint at a sub-optimal or ideological understanding of the prob
lem that is being deliberated on if sufficiently diverse preferences are assumed. In one experimental study, the citizen assembly process even led to increased polarization of politi
cal views and also of views on specific issues, which is opposed to the many speculative arguments that citizen assemblies will lead to decreased polar
ization. In any case, the effectiveness of deliberation seems to be highly sensitive to group composition and participants’ profiles. Moreover, the role of internal factors such as emotions, personal net
works, and power structures in the deliberation process is not well understood and warrants fur
ther study.
Lastly, the role of external events on the deliberation process, such as current political sentiment or major events, is understudied which the lack of studies on this topic
demonstrates. Democratic firms face many similar challenges because their governance form is democratic de
liberation. However, they operate distinctly from citizen assemblies because they are competing in market economies which exert pressure to perform, and their key performance indicator is not only an emerging consensus but financial viability. Therefore, as long as the overall economic sys
tem logic remains unchanged, one of the biggest challenges for democratic firms is simply to sur
vive in competitive markets. That said, existing literature provides qualitative insights into the internal operations and why democratic firms may be outcompeted by regular firms or not. Sometimes, democratic firms actually derive competitive advantages, for ex
ample, from knowledge sharing among each other. Among the reasons they fail to remain competitive are information processing fail
ures, which include, for instance, not shutting down unprofitable departments or products, emer
gent unintended hierarchies,and slow eco
innovation processes. It also has been observed that democratic firms do not achieve optimal team composition because instead of hiring the most qual
ified from the job market, there is a tendency to resort to personal networks to recruit new employ
ees, a practice that can lead to nepotism. In conclusion, bottom-up democratic systems face various pitfalls that warrant further study to understand the conditions that enable or hinder them. 3.7.2 Simulating challenges in citizen assemblies and democratic firms
In a next step, I map above introduced computational simulation approaches to the identified chal
lenges and outline how they might improve our understanding of those processes. Table 1 depicts an indicative and non-exhaustive mapping and elaborates on the reasoning of what each simulation approach could contribute. In summary, game theory approaches are appropriate for clarifying the role of individual incen
tives within deliberation processes. Stochastic game theory approaches are naturally good at rep resenting the large uncertainty that is inherent to them and at providing a first environment for repeated multi-agent interactions.
It shapes not only social dynamics, but political outcomes and economic up or downturns (for example by statements from prominent personalities). Hence,
LLMs could prove revolutionary in setting up agents that speak to each other and do not abstract away anymore from this central human aspect. Indeed, first studies implement such language based multi-agent models and even so in deliberation processes which seems a plausible step towards more elaborate political and economic settings. Agent-based mod
els (ABMs) are well-suited for simulating citizen assemblies and democratic firms because they are
bottom-up interactive agent models. In principle, any collective decision-making problem can be simulated using ABMs; the primary question is in the level of empirical validity and detail achieved
. This question of empirical validity constitutes perhaps the greatest methodological challenge in simulating citizen assemblies and democratic firms. Sim
ulation efforts therefore always benefit significantly from collaboration with empirical research groups and should actively pursue synergies between qualitative and quantitative evidence, as well as theory-building. The application of System Dynamics to citizen assemblies, democratic firms, and Artificial Utopias may not be immediately as intuitive as that of previous methods. Nonetheless, system dynamics could be particularly valuable when integrated with macroeconomic, ecological„ or policy dynam
ics that govern macro-level variables — such as population size, ecological reservoirs and resources,
institutions, fixed capital stocks, or monetary flows.
These dynamics, while external to bottom-up
deliberation processes, can influence them and thus constitute opportunities for integrated mod
elling approaches. Mapping Potential Challenges in Citizen Assemblies and Democratic Firms to Simulation Approaches
Micro Macro
Large Language models (LLMs)
LLMs can represent attitudes and personal views embodied in language. LLMs can represent biases and manipulation expressed in natural language. LLMs can help identify specific language and reasoning dynamics that sway opinions. LLMs may express emotions in language and simulate the interplay of language and personal relations. LLMs can help study how power is represented in language. LLMs can represent information in natural language and represent reasoning processes at the agent level. LLMs can help study communication and language aspects in the enforcement of
power. LLMs may explore explicit communication and rea soning processes and their role in
innovation. LLMs can represent interactive planning processes with explicit communication. LLMs can analyze communication styles within social networks to detect
nepotism. Citizen Assemblies Game Theory Ap proaches (GT)
System Dynamics (SD)
SD can help study whether consensus is sustainable in the face of external dis turbances. SD may represent external factors that bias assemblies one way or the other. SD again can clarify systemic and/or external feedbacks
that enable or block effective change of opinions. SD can study feedbacks caused by emotion across the population or the perception of overall sentiment. SD can study feedbacks of external factors that enforce or redistribute
power. SD can model system-wide information flows. SD can model governance interventions that deal with the distribution of power. SD, as always, may model system wide feedbacks that in hibit or enable in novation. SD can model
market-level and exogenous feedbacks that impact performance. SD can model policies against nepotism and their effectiveness at the systemic level. Agent-based Mod elling (ABM)
ABM can reveal emergent patterns of group behavior, such as polarization or coalition forma tion. ABM can explore how conflicting worldviews may introduce bias into decision-making. ABM can study whether certain group composition and network struc tures influence the spread of opinion change. ABMs can model how emotions are driven through interactions. ABM can study how power is embodied in social networks and enforced through them. ABM can model information flow between agents and examine how information changes when dissemi nated. ABM can study power structures in social networks. ABM is suited to model collective
ideation and intelli gence
ABMs can model markets consisting of many firms. ABM can depict social networks of employees to understand influence. Reinforcement Learning (RL)
RL can offer explicit modelling of distinct utility functions related to certain world views. RL could represent learning trajectory of an agent before entering the deliberation stage. RL could show how individual opinions evolve over time through trial and error in negotiations. RL can represent emotions in the objectives of agents and how they influence agents’ learning dynamics. RL can specify objective functions that represent power-seeking behavior. RL can study how behaviour and goals change in light of new information or obstruct informa tion processing. RL may represent objectives that have to do with powerseeking behavior. RL may align different objectives per agent under one firm-level objective. RL can model objectives at the individ ual level or the firmlevel for navigating the market. RL can conceptualize motivations for preferring personal contacts in employ ment. Consensus might not be achievable or might not be optimal
Bias and manipulation in citizen selection and in topics of discussion
Opinion change and questionable effectiveness of deliberation
Emotions and personal relations influence rhetoric, content, and the deliberation process
Power, intent to control deliberation, and deceit to achieve goals
Democratic Firms
Information processing failure
Emergent, unintended power structure
Slow (sustainable) innovation processes
Surviving in competitive economies
Nepotism tendency
GT can quantify the trade-offs between individual and collective payoffs in consensus-making. GT may clarify incentives and payoffs for participation. GT can elucidate incentives and payoffs for when participants change their minds. GT can define how emotions and personal relations influence perceived payoffs and costs in negotiations. GT may clarify the payoffs and risks agents are willing to take to gain power or when they cede it. GT can analyze pairwise and groupwise incentives to reject information.
GT can clarify incentives of different agents to gain or retain power. GT can analyze cooperative vs. competitive incentives in ideation and
execution. GT can assess cooperation or competition when groups decide a firm’s strat egy. GT may analyze benefits vs. costs of nepotistic practices. Table 1: Mapping of Issues in Citizen Assemblies and Democratic Firms to Appropriate Simulation Methods 4 Open questions and discussion
While the capability to create sophisticated simulations that generate insights is increasing at a rapid pace, this not only provides opportunities for Artificial Utopia research but raises many prac
tical and ethical questions. First, it is obvious that this capability is not equally distributed and in
creasingly in the hands of private companies. Most advances are generated in technology conglom
erates or well-funded start-ups, not in public institutions. And what can we expect from societal research that occurs within technology companies?. Often this research is guided by an engineering perspective and by growth imperatives: Who makes the most intelligent and most sophisticated models and who can turn it into profit? However, the question of ’What are we doing this for?’ - remains inadequately addressed apart from business case purposes and a few notable open-source exceptions. This is what Artificial
Utopia intends to change; Artificial Utopia articulates the desire to guide simulation intelligence by socially benevolent and human-centric visions and engages with social and economic creativity and reimagination. Second, the perspective presented here, including the selection of simulation approaches, can be critiqued on the grounds of ontological path-dependencies and contexts. This means that our cur rent possibilities for reimagining politics and the economy are constrained by the current political and economic contexts. And importantly they are also constrained by the simulation approaches that are available, because those come with implicit ontological assumptions. As
sume, for the sake of argument, a blank slate world with no, or drastically fewer, pre-existing sim
ulation and modelling approaches but still with substantial computational capability. Further as
sume, in such a scenario, a taskforce were allocated to reimagining alternative political institutions and economic mechanisms and so forth; it is at least plausible that the newly envisioned political and economic environments would give rise to a distinct, or at least partially different, set of sim
ulation needs compared to the tools we currently rely on, which have largely evolved from specific historical contexts and moments. For example, System Dynamics has its roots in managerial theory and was primarily developed for the cybernetic control of business processes. Game theory has often been refined through applications to conflict scenarios.. Simulation methods are hence at least partially influenced by the purposes they are conceived for. Ultimately, ’utopian’ applications might give rise to distinct simulation needs and therefore also distinct tools of exploration which are not captured
here. Third, it is not certain a priori whether more sophisticated social simulations serve scientific progress. Indeed, more complex models are of little advantage if the parameter and scenario space simply becomes more uncertain. Perhaps the biggest of all lessons from several decades Figure 3: Potential relationships between model sophistication and scientific advance
This figure presents two central questions in Artificial Utopia research and perhaps social simulation at large. Panel A sketches several potential stylized relationships between the complexity of agents in agent-models and scientific advance on the vertical axis. Neither dimension is precisely specified. Panel B similarly sketches a few possible relationships between the complexity of agent cognition and behavior and higher level emergence. An example for both panels would be the question of whether an agent-based model in economics becomes abetter model just because it is a high-dimensional one or whether high-dimensionality at the micro-economic level may even cloud aspects of the aggregate economy? There are no complete answers at this point but I postulate that these are two fundamental questions the agenda of Artificial Utopia should engage with. of complexity science is that already simple behavioral rules can lead to complex and emergent
outcomes. Examples are the logistic map whose chaotic orbits result in a complex fractal behavior space despite its apparent simplicity and the Game of Life resulting in life-like patterns despite be
ing grounded in extraordinarily simple rules of cell behavior. Therefore, with respect to Artificial Utopia, more complex agents enabled by AI do not necessarily lead to improved scientific insight or more complex emergent phenomena. This is also particularly important with respect to sustainability aspects of the research. Training AI applications is energy and resource intensive and it is not clear whether the ’costs’ are worth the insight. Figure 3
summarizes the uncertainty in the relationship between agent complexity and scientific advance as well as higher level emergence. To conclude, the future possibilities and trajectories for Artificial Utopia research are vast, varied and uncertain. In this article, I have chosen to focus specifically on bottom-up democratization pro
cesses, as they form a recurring theme in many scholarly and popular visions of utopia. However,
beyond democratisation and universal human rights, this focus should not be misunderstood as limiting the scope of what a utopia could entail. My goal has not been to prescribe a singular vision of utopia but rather to chart a path toward bridging the gap between the aspirations for utopia and our understanding of how such systems might work in practice.
By doing so, I hope to provide, or at least refine, tools that allow human societies to collectively steer their future trajectories. 5 Funding information
This project received funding from the REAL-project grant awarded to Prof. Julia Steinberger at the
University of Lausanne as well as Prof. Giorgios Kallis and Prof. Jason Hickel at the Autonomous
University of Barcelona (UAB). 6 Acknowledgments
I would like to thank Julia Steinberger very much for many helpful discussions related to this work and for feedback on a draft of this work.