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Probability-Based Estimation

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arXiv 2023年

作者： Heitzig, Jobst Potsdam Institute for Climate Impact Research Complexity Science FutureLab on Game Theory and Networks of Interacting Agents P. O. Box 60 12 03 Potsdam14412 Germany

We develop a theory of estimation when in addition to a sample of n observed outcomes the underlying probabilities of the observed outcomes are known, as is typically the case in the context of numerical simulation modeling, e.g. in epidemiology. For this enriched information framework, we design unbiased and consistent "probability-based" estimators whose variance vanish exponentially fast as n → ∞, as compared to the power-law decline of classical estimators' *** Codes 62F10 © 2023, CC BY.

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Improving International Climate Policy via Mutually Conditional Binding Commitments (Track 3)

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arXiv 2023年

作者： Heitzig, Jobst Oechssler, Jörg Pröschel, Christoph Ragavan, Niranjana Long Lo, Richie Yat FutureLab on Game Theory and Networks of Interacting Agents Complexity Science Department Potsdam Institute for Climate Impact Research Germany Alfred Weber Institute for Economics University of Heidelberg Germany Technical University of Berlin Germany Dyson Robot Learning Lab United Kingdom

This paper proposes enhancements to the RICE-N simulation and multi-agent reinforcement learning framework to improve the realism of international climate policy negotiations. Acknowledging the framework’s value, we highlight the necessity of significant enhancements to address the diverse array of factors in modeling climate negotiations. Building upon our previous work on the "Conditional Commitments Mechanism" (CCF mechanism) we discuss ways to bridge the gap between simulation and reality. We suggest the inclusion of a recommender or planner agent to enhance coordination, address the Real2Sim gap by incorporating social factors and non-party stakeholder sub-agents, and propose enhancements to the underlying Reinforcement Learning solution algorithm. These proposed improvements aim to advance the evaluation and formulation of negotiation protocols for more effective international climate policy decision-making in Rice-N. However, further experimentation and testing are required to determine the implications and effectiveness of these suggestions. © 2023, CC BY.

关键词： Decision making

Improving International Climate Policy via Mutually Conditional Binding Commitments

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arXiv 2023年

作者： Heitzig, Jobst Oechssler, Jörg Pröschel, Christoph Ragavan, Niranjana Lo, Yat Long FutureLab on Game Theory and Networks of Interacting Agents Complexity Science Department Potsdam Institute for Climate Impact Research Germany Alfred Weber Institute for Economics University of Heidelberg Germany Technical University of Berlin Germany Dyson Robot Learning Lab United Kingdom

The Paris Agreement, considered a significant milestone in climate negotiations, has faced challenges in effectively addressing climate change due to the unconditional nature of most Nationally Determined Contributions (NDCs). This has resulted in a prevalence of free-riding behavior among major polluters and a lack of concrete conditionality in NDCs. To address this issue, we propose the implementation of a decentralized, bottom-up approach called the Conditional Commitment Mechanism. This mechanism, inspired by the National Popular Vote Interstate Compact, offers flexibility and incentives for early adopters, aiming to formalize conditional cooperation in international climate policy. In this paper, we provide an overview of the mechanism, its performance in the AI4ClimateCooperation challenge, and discuss potential real-world implementation aspects. Prior knowledge of the climate mitigation collective action problem, basic economic principles, and game theory concepts are assumed. © 2023, CC BY.

关键词： game theory

Learning Interpretable Collective Variables for Spreading Processes on networks

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arXiv 2023年

作者： Lücke, Marvin Winkelmann, Stefanie Heitzig, Jobst Molkenthin, Nora Koltai, Péter Zuse Institute Berlin Germany FutureLab on Game Theory and Networks of Interacting Agents Potsdam Institute for Climate Impact Research Zuse Institute Berlin Germany Complexity Science Department Potsdam Institute for Climate Impact Research Germany Department of Mathematics University of Bayreuth Germany

Collective variables (CVs) are low-dimensional projections of high-dimensional system states. They are used to gain insights into complex emergent dynamical behaviors of processes on networks. The relation between CVs and network measures is not well understood and its derivation typically requires detailed knowledge of both the dynamical system and the network topology. In this work, we present a data-driven method for algorithmically learning and understanding CVs for binary-state spreading processes on networks of arbitrary topology. We demonstrate our method using four example networks: the stochastic block model, a ring-shaped graph, a random regular graph, and a scale-free network generated by the Albert-Barabási model. Our results deliver evidence for the existence of low-dimensional CVs even in cases that are not yet understood theoretically. © 2023, CC BY-NC-ND.

关键词： Network topology

Macroscopic approximation methods for the analysis of adaptive networked agent-based models: Example of a two-sector investment model

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Physical Review E 2020年第4期102卷 042311-042311页

作者： Jakob J. Kolb Finn Müller-Hansen Jürgen Kurths Jobst Heitzig FutureLab on Game Theory and Networks of Interacting Agents Potsdam Institute for Climate Impact Research 14473 Potsdam Germany

In this paper, we propose a statistical aggregation method for agent-based models with heterogeneous agents that interact both locally on a complex adaptive network and globally on a market. The method combines three approaches from statistical physics: (a) moment closure, (b) pair approximation of adaptive network processes, and (c) thermodynamic limit of the resulting stochastic process. As an example of use, we develop a stochastic agent-based model with heterogeneous households that invest in either a fossil-fuel- or renewables-based sector while allocating labor on a competitive market. Using the adaptive voter model, the model describes agents as social learners that interact on a dynamic network. We apply the approximation methods to derive a set of ordinary differential equations that approximate the macrodynamics of the model. A comparison of the reduced analytical model with numerical simulations shows that the approximation fits well for a wide range of parameters. The method makes it possible to use analytical tools to better understand the dynamical properties of models with heterogeneous agents on adaptive networks. We showcase this with a bifurcation analysis that identifies parameter ranges with multistabilities. The method can thus help to explain emergent phenomena from network interactions and make them mathematically traceable.

关键词： Econophysics Social dynamics Bifurcation analysis Decision making models Evolving network models Master equation

Climate Change Simulation games for Climate Action: A Case Study

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SSRN

SSRN 2023年

作者： Hüttel, Alexandra Heitzig, Jobst Smith, Keith Tamberg, Daniel FutureLab on Game Theory and Networks of Interacting Agents Potsdam Institute for Climate Impact Research Telegraphenberg A 31 Postfach 60 12 03 Potsdam14412 Germany International Political Economy and Environmental Politics ETH Zürich Haldeneggsteig 4 Zürich8092 Switzerland SCIARA GmbH Weberplatz 15 Potsdam14482 Germany

Simulation games can be adopted as knowledge tools to communicate uncertainties and complexities in social-ecological systems. Furthermore, simulation games can be leveraged to inform policy support and provide an experimental platform for novel explorations of complex systems. Here we evaluate the educational and research potential of simulation games, while also exploring the role of experimental learning shaping climate change commitments. As a case study, we focus on an original, browser-based, participatory climate change simulation which enables participants to explore and shape their personal future while "time travelling" into the next decades of the climate future. Based on a randomized controlled trial with a pre-/post-test design (N=392), we find that climate change simulations can be a useful tool to develop knowledge about climate change impacts and we uncover an essential role of knowledge generation in predicting climate change engagement. While we cannot confirm significant direct effects on concern about climate change, psychological distance from climate change, and perceived efficacy of climate action, we do find that responses to simulated climate change experiences appear to be related to educational attainment, which has important implications for the targeted use of simulations. We conclude with practical and policy implications for the outlined use of simulation games. © 2023, The Authors. All rights reserved.

关键词： Climate change

Large population limits of Markov processes on random networks

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arXiv 2022年

作者： Lücke, Marvin Heitzig, Jobst Koltai, Péter Molkenthin, Nora Winkelmann, Stefanie Zuse Institute Berlin Germany Department of Mathematics Free University of Berlin Germany FutureLab on Game Theory and Networks of Interacting Agents Potsdam Institute for Climate Impact Research Germany Complexity Science Department Potsdam Institute for Climate Impact Research Germany

We consider time-continuous Markovian discrete-state dynamics on random networks of interacting agents and study the large population limit. The dynamics are projected onto low-dimensional collective variables given by the shares of each discrete state in the system, or in certain subsystems, and general conditions for the convergence of the collective variable dynamics to a mean-field ordinary differential equation are proved. We discuss the convergence to this mean-field limit for a continuous-time noisy version of the so-called "voter model" on Erdős–Rényi random graphs, on the stochastic block model, and on random regular graphs. Moreover, a heterogeneous population of agents is studied. © 2022, CC BY-NC-ND.

关键词： Ordinary differential equations

Strategic Control for a Boltzmann Like Decision-Making Model

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SSRN

SSRN 2024年

作者： Venegas-Pineda, Luis Guillermo Jardón-Kojakhmetov, Hildeberto Engel, Maximilian Heitzig, Jobst Eser, Muhittin Cenk Cao, Ming Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Nijenborgh 9 Groningen9700 AK Netherlands Institute of Mathematics Freie Universität Berlin Arnimallee 6 Berlin14195 Germany Korteweg-de Vries Institute for Mathematics University of Amsterdam Science Park 105-107 Amsterdam1098 XG Netherlands FutureLab on Game Theory and Networks of Interacting Agents Complexity Science Department Potsdam Institute for Climate Impact Research PO Box 60 12 03 Potsdam14412 Germany Engineering and Technology Institute Groningen University of Groningen Nijenborgh 9 Groningen9700 AE Netherlands

We study a prototypical non-polynomial decision-making model for which agents in a population potentially alternate between two consumption strategies, one related to the exploitation of an unlimited but considerably expensive resource and the other a comparably cheaper but restricted and slowly renewable source. In particular, we study a model following a Boltzmann-like exploration policy, enhancing the accuracy at which the exchange rates are captured with respect to classical polynomial approaches by considering sigmoidal functions to represent the cost-profit relation in both exploit strategies. Additionally, given the intrinsic timescale separation between the decision-making process and recovery rates of the renewable resource, we use geometric singular perturbation theory to analyze the model. We further use numerical analysis to determine parameter ranges for which the model undergoes bifurcations. These bifurcations, being related to critical states of the system, are relevant to the fast transitions between strategies. Hence, we design controllers to regulate such rapid transitions by taking advantage of the system's criticality. © 2024, The Authors. All rights reserved.

关键词： Decision making

Strategic control for a Boltzmann like decision–making model

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arXiv 2024年

We study a prototypical non–polynomial decision–making model for which agents in a population potentially alternate between two consumption strategies, one related to the exploitation of an unlimited but considerably expensive resource and the other a comparably cheaper but restricted and slowly renewable source. In particular, we study a model following a Boltzmann–like exploration policy, enhancing the accuracy at which the exchange rates are captured with respect to classical polynomial approaches by considering sigmoidal functions to represent the cost–profit relation in both exploit strategies. Additionally, given the intrinsic timescale separation between the decision–making process and recovery rates of the renewable resource, we use geometric singular perturbation theory to analyze the model. We further use numerical analysis to determine parameter ranges for which the model undergoes bifurcations. These bifurcations, being related to critical states of the system, are relevant to the fast transitions between strategies. Hence, we design controllers to regulate such rapid transitions by taking advantage of the system’s criticality. © 2024, CC BY.

关键词： Perturbation techniques