Social Behavior Seminar
8 イベント
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セミナーThe decision intelligence of humans and machines
2026年7月24日(金) 10:30 - 11:30
Petter Holme (Professor, Department of Computer Science, Aalto University, Finland)
The event has been rescheduled from July 22 to July 24. To understand our near-future of artificial intelligence firmly integrated into many levels of social life, a challenge is to understand the differences and similarities between human and AI decision-making. In controlled laboratory settings assessing risk and uncertainty, LLMs demonstrate superhuman efficiency but fundamentally diverge from human behavior through a rigid hyper-rationality and an inability to disengage from obsolete strategies. However, when applied to messy, real-world dilemmas "in the wild," these models pivot to function as highly effective "satisficers". Human subjects consistently prefer this artificial counsel over human peer advice, noting its ability to carefully balance emotional context with logical constraints while actively reducing anxiety and regret. Ultimately, this synthesis shows that while AI can offer near-optimal laboratory performance and therapeutic impact in daily life, they also have a distinct lack of behavioral plasticity that we need to account for in models of the future.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Classical and quantum computing of Nash equilibria of two-player games
2026年6月25日(木) 10:30 - 11:30
エリック・ローツステット (理化学研究所 数理創造研究センター (iTHEMS) 数理展開部門 量子数理科学チーム 上級研究員)
Nash equilibrium is an important concept in game theory. However, finding mixed-strategy Nash equilibria is computationally hard even for relatively small games. I will review some aspects of the numerical computation of Nash equilibria of two-player games including the Lemke-Howson algorithm. I will also discuss preliminary attempts at solving the Nash equilibrium problem on a quantum computer by the quantum approximate optimization algorithm.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Cooperating on networks: inequality and social structure
2026年5月27日(水) 14:00 - 15:00
Manuel Staab (Lecturer, University of Queensland, Australia)
We analyse how inequality in endowments and social structure jointly affect individuals' ability to cooperate. Individuals repeatedly invest in a local public good ("cooperation'') in an environment that is described by a distribution of endowments and a network of beneficiaries. We measure the cooperativeness of an environment by the minimum discount factor needed to sustain (any) cooperation in equilibrium. We characterise the endowment distribution that maximises cooperativeness for any given network and the corresponding minimum discount factor. The latter is shown to be inversely proportional to the maximal index of the graph describing the network. The corresponding dominant eigenvalue of the adjacency matrix characterises the most cooperative income distribution. Moreover, we show that if an environment maximises cooperativeness (over all income distributions and networks of a certain size), then the network is described by a nested split graph. We further show that this is the same class of graphs that maximise welfare for any given discount factor, and yet, the most cooperative graph need not be equal to the most efficient.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Harnessing inequality for cooperation
2026年5月26日(火) 14:00 - 15:00
Maria Kleshnina (Senior Lecturer, School of Mathematical Sciences, Queensland University of Technology, Australia)
Inequality in resources is widely thought to undermine cooperation in social dilemmas. Yet cooperation among unequals is ubiquitous: between senior and junior colleagues, firms of different sizes, nations with asymmetric stakes. Here, we offer a resolution to this puzzle and derive a novel prediction: if the returns from cooperation are shared in accordance with the individuals' strategic incentives, inequality enables and strengthens cooperation. We develop a strategic framework to systematically explore cooperation when the returns of a joint project can be shared unevenly. We characterise the optimal sharing rule, which we call resilient sharing, that can sustain cooperation in repeated interactions when no other rule can. Resilient sharing equalises incentives to defect across players, but is neither egalitarian nor proportional. Surprisingly, it typically rewards weaker partners beyond their relative contributions. We show that cooperation can be sustained through direct reciprocity in any environment whenever individual contributions are sufficiently unequal. Evolutionary simulations and a behavioural experiment confirm the central prediction: under resilient sharing, cooperation succeeds among unequal partners where it fails among equals. This suggests that cooperation is more likely to evolve and thrive when individuals can vary contributions and divide returns flexibly, pointing to the role of institutions and norms in harnessing inequality to stabilize cooperation.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
On Mean-Field Games
2026年5月21日(木) 10:30 - 11:30
アントワーヌ・ディエズ (理化学研究所 数理創造研究センター (iTHEMS) 数理展開部門 数学応用研究チーム 研究員)
Stochastic differential games with a large number of players are notoriously challenging, both theoretically and numerically, typically when it comes to computing Nash equilibria. Yet, when many players interact somehow symmetrically by responding only to the average behavior of the others, the game can surprisingly become more tractable by taking the limit of an infinite number of players. This is in direct analogy with the so-called « mean-field theory » which simplifies the analysis of large systems of interacting particles in statistical physics. Introduced independently about two decades ago by Lasry and Lions (mathematics) and Caines, Huang and Malahamé (engineering), the theory of Mean-Field Games has since been greatly developed with various applications in engineering, economical, social and biological sciences. The goal of this short lecture is to introduce the key concepts, particularly the deep connections between game theory, Partial Differential Equations and stochastic analysis, and to showcase a few striking recent applications.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
The math that shows a perfect democracy is impossible
2026年4月23日(木) 10:30 - 11:30
ブライアン・アンドリュー・ミンツ (理化学研究所 数理創造研究センター (iTHEMS) 数理展開部門 数理社会科学チーム 特別研究員)
Groups need to make decisions, and there are a wide variety of ways this can be done, each maximizing different notions of fairness. Social Choice Theory provides a mathematical framework to investigate these possibilities rigorously. Infamous for its many impossibility results, this topic reveals some fundamental limits to democracy. Beyond this, we'll discuss potential resolutions to these problems, as well as their real world implications.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Introduction to Game Theory #3
2025年11月27日(木) 11:00 - 12:00
村瀬 洋介 (理化学研究所 数理創造研究センター (iTHEMS) 数理展開部門 数理社会科学チーム チームディレクター)
An introductory lecture on game theory to promote potential interdisciplinary collaborations. No prior knowledge is required — the lecture is intended for non-experts. We will cover the fundamental concepts to help you build an intuitive understanding of how game theory analyzes strategic interactions. After briefly reviewing the previous lectures, we will talk about repeated games, where players are engaged in games repeatedly.
会場: via Zoom (メイン会場) / セミナー室 (359号室)
イベント公式言語: 英語
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セミナー
Introduction to Game Theory #2
2025年11月6日(木) 11:00 - 12:00
村瀬 洋介 (理化学研究所 数理創造研究センター (iTHEMS) 数理展開部門 数理社会科学チーム チームディレクター)
An introductory lecture on game theory to promote potential interdisciplinary collaborations. No prior knowledge is required — the lecture is intended for non-experts. We will cover the fundamental concepts to help you build an intuitive understanding of how game theory analyzes strategic interactions. After briefly reviewing the previous lecture, we will discuss mixed-strategy Nash equilibria and their computational complexity.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
8 イベント