-
From Firms to Computation: AI Governance and the Evolution of Institutions
Authors:
Michael S. Harre
Abstract:
The integration of agential artificial intelligence into socioeconomic systems requires us to reexamine the evolutionary processes that describe changes in our economic institutions. This article synthesizes three frameworks: multi-level selection theory, Aoki's view of firms as computational processes, and Ostrom's design principles for robust institutions. We develop a framework where selection…
▽ More
The integration of agential artificial intelligence into socioeconomic systems requires us to reexamine the evolutionary processes that describe changes in our economic institutions. This article synthesizes three frameworks: multi-level selection theory, Aoki's view of firms as computational processes, and Ostrom's design principles for robust institutions. We develop a framework where selection operates concurrently across organizational levels, firms implement distributed inference via game-theoretic architectures, and Ostrom-style rules evolve as alignment mechanisms that address AI-related risks. This synthesis yields a multi-level Price equation expressed over nested games, providing quantitative metrics for how selection and governance co-determine economic outcomes. We examine connections to Acemoglu's work on inclusive institutions, analyze how institutional structures shape AI deployment, and demonstrate the framework's explanatory power via case studies. We conclude by proposing a set of design principles that operationalize alignment between humans and AI across institutional layers, enabling scalable, adaptive, and inclusive governance of agential AI systems. We conclude with practical policy recommendations and further research to extend these principles into real-world implementation.
△ Less
Submitted 17 July, 2025;
originally announced July 2025.
-
Artificial Theory of Mind and Self-Guided Social Organisation
Authors:
Michael S. Harré,
Jaime Ruiz-Serra,
Catherine Drysdale
Abstract:
One of the challenges artificial intelligence (AI) faces is how a collection of agents coordinate their behaviour to achieve goals that are not reachable by any single agent. In a recent article by Ozmen et al this was framed as one of six grand challenges: That AI needs to respect human cognitive processes at the human-AI interaction frontier. We suggest that this extends to the AI-AI frontier an…
▽ More
One of the challenges artificial intelligence (AI) faces is how a collection of agents coordinate their behaviour to achieve goals that are not reachable by any single agent. In a recent article by Ozmen et al this was framed as one of six grand challenges: That AI needs to respect human cognitive processes at the human-AI interaction frontier. We suggest that this extends to the AI-AI frontier and that it should also reflect human psychology, as it is the only successful framework we have from which to build out. In this extended abstract we first make the case for collective intelligence in a general setting, drawing on recent work from single neuron complexity in neural networks and ant network adaptability in ant colonies. From there we introduce how species relate to one another in an ecological network via niche selection, niche choice, and niche conformity with the aim of forming an analogy with human social network development as new agents join together and coordinate. From there we show how our social structures are influenced by our neuro-physiology, our psychology, and our language. This emphasises how individual people within a social network influence the structure and performance of that network in complex tasks, and that cognitive faculties such as Theory of Mind play a central role. We finish by discussing the current state of the art in AI and where there is potential for further development of a socially embodied collective artificial intelligence that is capable of guiding its own social structures.
△ Less
Submitted 13 November, 2024;
originally announced November 2024.
-
An AI Theory of Mind Will Enhance Our Collective Intelligence
Authors:
Michael S. Harré,
Catherine Drysdale,
Jaime Ruiz-Serra
Abstract:
Collective intelligence plays a central role in many fields, from economics and evolutionary theory to neural networks and eusocial insects, and is also core to work on emergence and self-organisation in complex-systems theory. However, in human collective intelligence there is still much to understand about how specific psychological processes at the individual level give rise to self-organised s…
▽ More
Collective intelligence plays a central role in many fields, from economics and evolutionary theory to neural networks and eusocial insects, and is also core to work on emergence and self-organisation in complex-systems theory. However, in human collective intelligence there is still much to understand about how specific psychological processes at the individual level give rise to self-organised structures at the social level. Psychological factors have so far played a minor role in collective-intelligence studies because the principles are often general and applicable to agents without sophisticated psychologies. We emphasise, with examples from other complex adaptive systems, the broad applicability of collective-intelligence principles, while noting that mechanisms and time scales differ markedly between cases. We review evidence that flexible collective intelligence in human social settings is improved by a particular cognitive tool: our Theory of Mind. We then hypothesise that AIs equipped with a theory of mind will enhance collective intelligence in ways similar to human contributions. To make this case, we step back from the algorithmic basis of AI psychology and consider the large-scale impact AI can have as agential actors in a 'social ecology' rather than as mere technological tools. We identify several key characteristics of psychologically mediated collective intelligence and show that the development of a Theory of Mind is crucial in distinguishing human social collective intelligence from more general forms. Finally, we illustrate how individuals, human or otherwise, integrate within a collective not by being genetically or algorithmically programmed, but by growing and adapting into the socio-cognitive niche they occupy. AI can likewise inhabit one or multiple such niches, facilitated by a Theory of Mind.
△ Less
Submitted 7 July, 2025; v1 submitted 13 November, 2024;
originally announced November 2024.
-
Factorised Active Inference for Strategic Multi-Agent Interactions
Authors:
Jaime Ruiz-Serra,
Patrick Sweeney,
Michael S. Harré
Abstract:
Understanding how individual agents make strategic decisions within collectives is important for advancing fields as diverse as economics, neuroscience, and multi-agent systems. Two complementary approaches can be integrated to this end. The Active Inference framework (AIF) describes how agents employ a generative model to adapt their beliefs about and behaviour within their environment. Game theo…
▽ More
Understanding how individual agents make strategic decisions within collectives is important for advancing fields as diverse as economics, neuroscience, and multi-agent systems. Two complementary approaches can be integrated to this end. The Active Inference framework (AIF) describes how agents employ a generative model to adapt their beliefs about and behaviour within their environment. Game theory formalises strategic interactions between agents with potentially competing objectives. To bridge the gap between the two, we propose a factorisation of the generative model whereby each agent maintains explicit, individual-level beliefs about the internal states of other agents, and uses them for strategic planning in a joint context. We apply our model to iterated general-sum games with two and three players, and study the ensemble effects of game transitions, where the agents' preferences (game payoffs) change over time. This non-stationarity, beyond that caused by reciprocal adaptation, reflects a more naturalistic environment in which agents need to adapt to changing social contexts. Finally, we present a dynamical analysis of key AIF quantities: the variational free energy (VFE) and the expected free energy (EFE) from numerical simulation data. The ensemble-level EFE allows us to characterise the basins of attraction of games with multiple Nash Equilibria under different conditions, and we find that it is not necessarily minimised at the aggregate level. By integrating AIF and game theory, we can gain deeper insights into how intelligent collectives emerge, learn, and optimise their actions in dynamic environments, both cooperative and non-cooperative.
△ Less
Submitted 20 May, 2025; v1 submitted 11 November, 2024;
originally announced November 2024.
-
Comparative analysis of stationarity for Bitcoin and the S&P500
Authors:
Yaoyue Tang,
Karina Arias-Calluari,
Michael S. Harré
Abstract:
This paper compares and contrasts stationarity between the conventional stock market and cryptocurrency. The dataset used for the analysis is the intraday price indices of the S&P500 from 1996 to 2023 and the intraday Bitcoin indices from 2019 to 2023, both in USD. We adopt the definition of `wide sense stationary', which constrains the time independence of the first and second moments of a time s…
▽ More
This paper compares and contrasts stationarity between the conventional stock market and cryptocurrency. The dataset used for the analysis is the intraday price indices of the S&P500 from 1996 to 2023 and the intraday Bitcoin indices from 2019 to 2023, both in USD. We adopt the definition of `wide sense stationary', which constrains the time independence of the first and second moments of a time series. The testing method used in this paper follows the Wiener-Khinchin Theorem, i.e., that for a wide sense stationary process, the power spectral density and the autocorrelation are a Fourier transform pair. We demonstrate that localized stationarity can be achieved by truncating the time series into segments, and for each segment, detrending and normalizing the price return are required. These results show that the S&P500 price return can achieve stationarity for the full 28-year period with a detrending window of 12 months and a constrained normalization window of 10 minutes. With truncated segments, a larger normalization window can be used to establish stationarity, indicating that within the segment the data is more homogeneous. For Bitcoin price return, the segment with higher volatility presents stationarity with a normalization window of 60 minutes, whereas stationarity cannot be established in other segments.
△ Less
Submitted 6 August, 2024;
originally announced August 2024.
-
Stylized Facts of High-Frequency Bitcoin Time Series
Authors:
Yaoyue Tang,
Karina Arias-Calluari,
M. N. Najafi,
Michael S. Harré,
Fernando Alonso-Marroquin
Abstract:
This paper analyses the high-frequency intraday Bitcoin dataset from 2019 to 2022. During this time frame, the Bitcoin market index exhibited two distinct periods, 2019-20 and 2021-22, characterized by an abrupt change in volatility. The Bitcoin price returns for both periods can be described by an anomalous diffusion process, transitioning from subdiffusion for short intervals to weak superdiffus…
▽ More
This paper analyses the high-frequency intraday Bitcoin dataset from 2019 to 2022. During this time frame, the Bitcoin market index exhibited two distinct periods, 2019-20 and 2021-22, characterized by an abrupt change in volatility. The Bitcoin price returns for both periods can be described by an anomalous diffusion process, transitioning from subdiffusion for short intervals to weak superdiffusion over longer time intervals. The characteristic features related to this anomalous behavior studied in the present paper include heavy tails, which can be described using a $q$-Gaussian distribution and correlations. When we sample the autocorrelation of absolute returns, we observe a power-law relationship, indicating time dependence in both periods initially. The ensemble autocorrelation of the returns decays rapidly. We fitted the autocorrelation with a power law to capture the decay and found that the second period experienced a slightly higher decay rate. The further study involves the analysis of endogenous effects within the Bitcoin time series, which are examined through detrending analysis. We found that both periods are multifractal and present self-similarity in the detrended probability density function (PDF). The Hurst exponent over short time intervals shifts from less than 0.5 ($\sim$ 0.42) in Period 1 to closer to 0.5 in Period 2 ($\sim$ 0.49), indicating that the market has gained efficiency over time.
△ Less
Submitted 22 June, 2025; v1 submitted 19 February, 2024;
originally announced February 2024.
-
The impact of social influence in Australian real-estate: market forecasting with a spatial agent-based model
Authors:
Benjamin Patrick Evans,
Kirill Glavatskiy,
Michael S. Harré,
Mikhail Prokopenko
Abstract:
Housing markets are inherently spatial, yet many existing models fail to capture this spatial dimension. Here we introduce a new graph-based approach for incorporating a spatial component in a large-scale urban housing agent-based model (ABM). The model explicitly captures several social and economic factors that influence the agents' decision-making behaviour (such as fear of missing out, their t…
▽ More
Housing markets are inherently spatial, yet many existing models fail to capture this spatial dimension. Here we introduce a new graph-based approach for incorporating a spatial component in a large-scale urban housing agent-based model (ABM). The model explicitly captures several social and economic factors that influence the agents' decision-making behaviour (such as fear of missing out, their trend following aptitude, and the strength of their submarket outreach), and interprets these factors in spatial terms. The proposed model is calibrated and validated with the housing market data for the Greater Sydney region. The ABM simulation results not only include predictions for the overall market, but also produce area-specific forecasting at the level of local government areas within Sydney as arising from individual buy and sell decisions. In addition, the simulation results elucidate agent preferences in submarkets, highlighting differences in agent behaviour, for example, between first-time home buyers and investors, and between both local and overseas investors.
△ Less
Submitted 9 February, 2021; v1 submitted 15 September, 2020;
originally announced September 2020.
-
The Polycentric Dynamics of Melbourne and Sydney: Suburb attractiveness divides a city at the home ownership level
Authors:
Emanuele Crosato,
Mikhail Prokopenko,
Michael S. Harré
Abstract:
Urban dynamics in large metropolitan areas result from complex interactions across social, economic, and political factors, including population distribution, flows of wealth, and infrastructure requirements. We develop a Census-calibrated model of urban dynamics for the Greater Sydney and Melbourne areas for 2011 and 2016, highlighting the evolution of population distributions and the housing mar…
▽ More
Urban dynamics in large metropolitan areas result from complex interactions across social, economic, and political factors, including population distribution, flows of wealth, and infrastructure requirements. We develop a Census-calibrated model of urban dynamics for the Greater Sydney and Melbourne areas for 2011 and 2016, highlighting the evolution of population distributions and the housing market structure in these two cities in terms of their mortgage and rent distributions. We show that there is a tendency to homophily between renters and mortgage holders: renters tend to cluster nearer commercial centres, whereas mortgagors tend to populate the outskirts of these centres. We also identify a critical threshold at which the long-term evolution of these two cities will bifurcate between a `sprawling' and a `polycentric' configuration, showing that both cities lie on the polycentric side of the critical point in the long-run. Importantly, there is a divergence of these centric tendencies between the renters and mortgage holders. The polycentric patterns characterising the mortgagors are focused around commercial centres, and we show that the emergent housing patterns follow the major transport routes through the cities.
△ Less
Submitted 9 August, 2020;
originally announced August 2020.
-
Stationarity of the detrended price return in stock markets
Authors:
Karina Arias-Calluari,
Morteza. N. Najafi,
Michael S. Harré,
Fernando Alonso-Marroquin
Abstract:
This paper proposes a governing equation for stock market indexes that accounts for non-stationary effects. This is a linear Fokker-Planck equation (FPE) that describes the time evolution of the probability distribution function (PDF) of the price return. By applying Ito's lemma, this FPE is associated with a stochastic differential equation (SDE) that models the time evolution of the price return…
▽ More
This paper proposes a governing equation for stock market indexes that accounts for non-stationary effects. This is a linear Fokker-Planck equation (FPE) that describes the time evolution of the probability distribution function (PDF) of the price return. By applying Ito's lemma, this FPE is associated with a stochastic differential equation (SDE) that models the time evolution of the price return in a fashion different from the classical Black-Scholes equation. Both FPE and SDE equations account for a deterministic part or trend, and a stationary, stochastic part as a q-Gaussian noise. The model is validated using the S\&P500 index's data. After removing the trend from the index, we show that the detrended part is stationary by evaluating the Hurst exponent of the multifractal time series, its power spectrum, and its autocorrelation.
△ Less
Submitted 22 August, 2020; v1 submitted 2 October, 2019;
originally announced October 2019.
-
Singularities and Catastrophes in Economics: Historical Perspectives and Future Directions
Authors:
Michael S. Harré,
Adam Harris,
Scott McCallum
Abstract:
Economic theory is a mathematically rich field in which there are opportunities for the formal analysis of singularities and catastrophes. This article looks at the historical context of singularities through the work of two eminent Frenchmen around the late 1960s and 1970s. René Thom (1923-2002) was an acclaimed mathematician having received the Fields Medal in 1958, whereas Gérard Debreu (1921-2…
▽ More
Economic theory is a mathematically rich field in which there are opportunities for the formal analysis of singularities and catastrophes. This article looks at the historical context of singularities through the work of two eminent Frenchmen around the late 1960s and 1970s. René Thom (1923-2002) was an acclaimed mathematician having received the Fields Medal in 1958, whereas Gérard Debreu (1921-2004) would receive the Nobel Prize in economics in 1983. Both were highly influential within their fields and given the fundamental nature of their work, the potential for cross-fertilisation would seem to be quite promising. This was not to be the case: Debreu knew of Thom's work and cited it in the analysis of his own work, but despite this and other applied mathematicians taking catastrophe theory to economics, the theory never achieved a lasting following and relatively few results were published. This article reviews Debreu's analysis of the so called ${\it regular}$ and ${\it crtitical}$ economies in order to draw some insights into the economic perspective of singularities before moving to how singularities arise naturally in the Nash equilibria of game theory. Finally a modern treatment of stochastic game theory is covered through recent work on the quantal response equilibrium. In this view the Nash equilibrium is to the quantal response equilibrium what deterministic catastrophe theory is to stochastic catastrophe theory, with some caveats regarding when this analogy breaks down discussed at the end.
△ Less
Submitted 12 July, 2019;
originally announced July 2019.
-
Entropy and Transfer Entropy: The Dow Jones and the build up to the 1997 Asian Crisis
Authors:
Michael S. Harre
Abstract:
Entropy measures in their various incarnations play an important role in the study of stochastic time series providing important insights into both the correlative and the causative structure of the stochastic relationships between the individual components of a system. Recent applications of entropic techniques and their linear progenitors such as Pearson correlations and Granger causality have h…
▽ More
Entropy measures in their various incarnations play an important role in the study of stochastic time series providing important insights into both the correlative and the causative structure of the stochastic relationships between the individual components of a system. Recent applications of entropic techniques and their linear progenitors such as Pearson correlations and Granger causality have have included both normal as well as critical periods in a system's dynamical evolution. Here I measure the entropy, Pearson correlation and transfer entropy of the intra-day price changes of the Dow Jones Industrial Average in the period immediately leading up to and including the Asian financial crisis and subsequent mini-crash of the DJIA on the 27th October 1997. I use a novel variation of transfer entropy that dynamically adjusts to the arrival rate of individual prices and does not require the binning of data to show that quite different relationships emerge from those given by the conventional Pearson correlations between equities. These preliminary results illustrate how this modified form of the TE compares to results using Pearson correlation.
△ Less
Submitted 19 November, 2018;
originally announced November 2018.
-
Multi-agent Economics and the Emergence of Critical Markets
Authors:
Michael S. Harré
Abstract:
The dual crises of the sub-prime mortgage crisis and the global financial crisis has prompted a call for explanations of non-equilibrium market dynamics. Recently a promising approach has been the use of agent based models (ABMs) to simulate aggregate market dynamics. A key aspect of these models is the endogenous emergence of critical transitions between equilibria, i.e. market collapses, caused…
▽ More
The dual crises of the sub-prime mortgage crisis and the global financial crisis has prompted a call for explanations of non-equilibrium market dynamics. Recently a promising approach has been the use of agent based models (ABMs) to simulate aggregate market dynamics. A key aspect of these models is the endogenous emergence of critical transitions between equilibria, i.e. market collapses, caused by multiple equilibria and changing market parameters. Several research themes have developed microeconomic based models that include multiple equilibria: social decision theory (Brock and Durlauf), quantal response models (McKelvey and Palfrey), and strategic complementarities (Goldstein). A gap that needs to be filled in the literature is a unified analysis of the relationship between these models and how aggregate criticality emerges from the individual agent level. This article reviews the agent-based foundations of markets starting with the individual agent perspective of McFadden and the aggregate perspective of catastrophe theory emphasising connections between the different approaches. It is shown that changes in the uncertainty agents have in the value of their interactions with one another, even if these changes are one-sided, plays a central role in systemic market risks such as market instability and the twin crises effect. These interactions can endogenously cause crises that are an emergent phenomena of markets.
△ Less
Submitted 5 September, 2018;
originally announced September 2018.
-
An interview based study of pioneering experiences in teaching and learning Complex Systems in Higher Education
Authors:
Joseph T. Lizier,
Michael S. Harré,
Melanie Mitchell,
Simon DeDeo,
Conor Finn,
Kristian Lindgren,
Amanda L. Lizier,
Hiroki Sayama
Abstract:
Due to the interdisciplinary nature of complex systems as a field, students studying complex systems at University level have diverse disciplinary backgrounds. This brings challenges (e.g. wide range of computer programming skills) but also opportunities (e.g. facilitating interdisciplinary interactions and projects) for the classroom. However, there is little published regarding how these challen…
▽ More
Due to the interdisciplinary nature of complex systems as a field, students studying complex systems at University level have diverse disciplinary backgrounds. This brings challenges (e.g. wide range of computer programming skills) but also opportunities (e.g. facilitating interdisciplinary interactions and projects) for the classroom. However, there is little published regarding how these challenges and opportunities are handled in teaching and learning Complex Systems as an explicit subject in higher education, and how this differs in comparison to other subject areas. We seek to explore these particular challenges and opportunities via an interview-based study of pioneering teachers and learners (conducted amongst the authors) regarding their experiences. We compare and contrast those experiences, and analyse them with respect to the educational literature. Our discussions explored: approaches to curriculum design, how theories/models/frameworks of teaching and learning informed decisions and experience, how diversity in student backgrounds was addressed, and assessment task design. We found a striking level of commonality in the issues expressed as well as the strategies to handle them, for example a significant focus on problem-based learning, and the use of major student-led creative projects for both achieving and assessing learning outcomes.
△ Less
Submitted 7 February, 2018;
originally announced February 2018.