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Optimizing testing strategies for early detection of disease outbreaks in animal trade networks via MCMC

CHAOS 2023年第4期33卷 1-10页

作者： Ansari, Sara Heitzig, Jobst Moosavi, Mohammad R. Shiraz Univ Sch Elect & Comp Engn Dept Comp Sci & Engn Shiraz *** Iran Potsdam Inst Climate Impact Res FutureLab Game Theory & Networks Interacting Agent D-14473 Potsdam Germany

The animal trades between farms and other livestock holdings form a complex livestock trade network. The movement of animals between trade actors plays an important role in the spread of infectious diseases among premises. Particularly, the outbreak of silent diseases that have no clinically obvious symptoms in the animal trade system should be diagnosed by taking special tests. In practice, the authorities regularly conduct examinations on a random number of farms to make sure that there was no outbreak in the system. However, these actions, which aim to discover and block a disease cascade, are yet far from the effective and optimum solution and often fail to prevent epidemics. A testing strategy is defined as making decisions about distributing the fixed testing budget N between farms/nodes in the network. In this paper, first, we apply different heuristics for selecting sentinel farms on real and synthetic pig-trade networks and evaluate them by simulating disease spreading via the SI epidemic model. Later, we propose a Markov chain Monte Carlo (MCMC) based testing strategy with the aim of early detection of outbreaks. The experimental results show that the proposed method can reasonably well decrease the size of the outbreak on both the realistic synthetic and real trade data. A targeted selection of an N / 52 fraction of nodes in the real pig-trade network based on the MCMC or simulated annealing can improve the performance of a baseline strategy by 89%. The best heuristic-based testing strategy results in a 75% reduction in the average size of the outbreak compared to that of the baseline testing strategy.

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The Rule-Tool-User Nexus in Digital Collective Decisions Blue Sky Ideas Track 22

The Rule-Tool-User Nexus in Digital Collective Decisions Blu...

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22nd International Conference on Autonomous agents and Multiagent Systems, AAMAS 2023

作者： Terzopoulou, Zoi Keijzer, Marijn A. Sreedurga, Gogulapati Heitzig, Jobst GATE University of Lyon - Saint-Etienne France Institute for Advanced Study in Toulouse France Indian Institute of Science India FutureLab on Game Theory and Networks of Interactive Agents Potsdam Institute for Climate Impact Research Germany

Collective decision making is experiencing a digital revolution. Online platforms offer to spread information, help groups make better decisions, incentivize people to exchange arguments, and force policy makers to take into account the public opinion. Social choice theory, a sub-discipline of economics, typically analyzes collective decisions, but rather overlooks the multitude of coalescent elements playing a role in them. To ensure that digital democracy is effective and scientifically grounded, we offer an original view of collective decisions as complex systems, and propose to study the systems' components in parallel with the interactions between them. We identify three eminent components: the individual agents in a group, i.e., some users of a platform, the voting rule that determines the final collective decisions, and the tools via which the users practically engage with a platform. The success of digital democracy relies on interdisciplinary and cross-methodological research. We indicate several paths in this direction. © 2023 International Foundation for Autonomous agents and Multiagent Systems (***). All rights reserved.

关键词： Decision making

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A global threshold model of enabling conditions for social tipping in pro-environmental behaviours - the role of sea level rise anticipation and climate change concern

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EARTH SYSTEM DYNAMICS 2025年第2期16卷 545-564页

作者： Smith, E. Keith Wiedermann, Marc Donges, Jonathan F. Heitzig, Jobst Winkelmann, Ricarda Swiss Fed Inst Technol Int Polit Econ & Environm Polit Zurich Switzerland Leibniz Assoc FutureLab Game Theory & Networks Interacting Agent Potsdam Inst Climate Impact Res Complex Sci Potsdam Germany Robert Koch Inst Berlin Germany Humboldt Univ Inst Theoret Biol Berlin Germany Leibniz Assoc Potsdam Inst Climate Impact Res FutureLab Earth Resilience Anthropocene Earth Syst Anal Potsdam Germany Univ Potsdam Inst Phys & Astron Potsdam Germany Stockholm Univ Stockholm Resilience Ctr Stockholm Sweden Max Planck Inst Geoanthropol Dept Integrat Earth Syst Sci Jena Germany

Effective climate change mitigation necessitates swift societal transformations. Positive social tipping processes, where small triggers initiate qualitative systemic shifts, are potential key mechanisms towards instigating the desired emissions mitigation. A necessary foundation for societal tipping processes is the creation of enabling conditions. Here, we assess future sea level rise estimates and social survey data within the framework of a network-based threshold model to exemplify the enabling conditions for tipping processes. We find that in many countries, the level of climate change concern is already sufficient, suggesting the enabling conditions and opportunities for social activation already exist. Further, drawing upon the interrelation between climate change concern and anticipation of future sea level rise, we report three qualitative classes of tipping potential that are regionally clustered, with the greatest potential for tipping in western Pacific Rim and East Asian countries. These findings propose a transformative pathway where climate change concern increases the social tipping potential, while extended anticipation time horizons can trigger the system towards an alternative trajectory of larger social activation for climate change mitigation.

关键词： Miocene

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Probability-Based Estimation

arXiv

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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)

arXiv

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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

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Improving International Climate Policy via Mutually Conditional Binding Commitments

arXiv

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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

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Learning Interpretable Collective Variables for Spreading Processes on networks

arXiv

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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

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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

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Fair group decisions via non-deterministic proportional consensus

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SOCIAL CHOICE AND WELFARE 2025年第1-2期64卷 345-371页

作者： Heitzig, Jobst Simmons, Forest W. Constantino, Sara M. Potsdam Inst Climate Impact Res FutureLab Game Theory & Networks Interacting Agent POB 60 12 03 D-14412 Potsdam Germany Portland Community Coll Liberal Arts & Math Div Cascade Campus705 N Killingsworth St Portland OR 97217 USA Northeastern Univ Sch Publ Policy & Urban Affairs 310 Renaissance Pk Boston MA 02115 USA Northeastern Univ Dept Psychol 105 Forsyth St Boston MA 02115 USA Princeton Univ Sch Publ & Int Affairs 20 Prospect Ave Princeton NJ 08540 USA

Are there group decision methods which (i) give everyone, including minorities, an equal share of effective decision power even when voters act strategically, (ii) promote consensus and equality, rather than polarization and inequality, and (iii) do not favour the status quo or rely too much on chance? We describe two non-deterministic group decision methods that meet these criteria, one based on automatic bargaining over lotteries, the other on conditional commitments to approve compromise options. Using theoretical analysis, agent-based simulations and a behavioral experiment, we show that these methods prevent majorities from consistently suppressing minorities, which can happen in deterministic methods, and keeps proponents of the status quo from blocking decisions, as in other consensus-based approaches. Our simulations show that these methods achieve aggregate welfare comparable to common voting methods, while employing chance judiciously, and that the welfare costs of fairness and consensus are small compared to the inequality costs of majoritarianism. In an incentivized experiment with naive participants, we find that a sizable fraction of participants prefers to use a non-deterministic voting method over Plurality Voting to allocate monetary resources. However, this depends critically on their position within the group. Those in the majority show a strong preference for majoritarian voting methods.

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Climate Change Simulation games for Climate Action: A Case Study

SSRN

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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

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