Social Behavior Seminar
13 イベント
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セミナー
Behaviorally Informed Institutional Design with Game Theory and LLMs
2026年10月29日(木) 14:30 - 15:30
森 隆太郎 (理化学研究所 脳神経科学研究センター (CBS) 計算論的集団力学連携ユニット 基礎科学特別研究員)
The event will start 30 minutes before initially announced. How can we design social institutions that account for how people actually make decisions? Behavioral research has revealed substantial heterogeneity in preferences and decision-making that traditional economic models often abstract away. Understanding these tendencies may open new possibilities for improving collective outcomes. In this talk, I present two projects addressing complementary aspects of this broader goal. First, I investigate how real-time interaction facilitates cooperation in a one-shot social dilemma, where purely self-interested behavior predicts only mutual defection. Experiments show that real-time interaction yields higher mutual cooperation, primarily through stronger reciprocity by followers. A game-theoretic model suggests that flexible decision timing helps people infer others' cooperative preferences from their actions and coordinate their responses. This illustrates how institutional tweaks can leverage existing cooperative tendencies. Second, I introduce Cross-Centaur, an LLM-based model of individual decision-making across diverse domains. We construct a dataset in which the same participants complete tasks spanning learning, decisions under risk and delay, reasoning, and social decision-making, and use it to fine-tune an LLM. The model uses behavior in one task to better predict the same individual's decisions in another, a step toward more flexible, generalizable models of decision-making. Together, these projects explore how understanding human decision-making may inform the design of better social institutions.
会場: セミナー室 (359号室) 3階 359号室とZoomのハイブリッド開催 / via Zoom
イベント公式言語: 英語
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セミナー
An Origin Story for Money
2026年10月22日(木) 15:00 - 16:00
Stephen Marsland (Professor, Victoria University of Wellington, New Zealand)
In indirect reciprocity a reputation is used to enable donors to choose whether or not to provide costly help to potential recipients. Often this reputation is binary — good or bad — and is maintained by a third party who oversees all interactions. I will introduce extensions to this system that enables money to be viewed as a reputation and investigate how this helps us identify how systems of money may have first been invented.
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
Evolutionary Dynamics and Information Theory
2026年10月1日(木) 15:00 - 16:00
Marc Harper (Data Scientist, Google Inc., USA)
We'll discuss some of the history of connections among information theory, evolutionary dynamics, and statistical inference, drawing analogies between concepts and results in these fields, for example between the replicator equation (a model of natural selection) and Bayesian inference. Time permitting, we'll continue with generalizations to finite populations with mutation and/or variations including an analogy between epigenetics from biological evolution and momentum from machine learning.
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
Can Electric Sheep Evolve? Engineering Evolution in Populations of Language-Model Agents
2026年9月3日(木) 15:00 - 16:00
イヴァン・ロミチ (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 研究員)
The idea of artificial intelligence that evolves has long been a theme in science fiction. It also has roots in early research on complex adaptive systems and artificial societies, including Holland’s Echo framework and Epstein and Axtell’s Sugarscape, which modeled adaptation, resource competition, reproduction, and cultural transmission among artificial agents. Contemporary LLM development, however, has largely centered on pretrained models and gradient-based post-training, while explicitly Darwinian population processes have remained comparatively peripheral. This seminar reviews an emerging literature on evolutionary optimization, cultural transmission, and population dynamics in LLM-based agents. I distinguish systems that merely borrow evolutionary terminology from those implementing meaningful variation, heredity, and reproduction. I then assess the strengths and limitations of current studies and ask whether such processes can support cumulative adaptation, collective intelligence, and potentially new levels of organization among artificial agents.
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
How Reputation Sustains Cooperation: Mathematical Theories of Indirect Reciprocity
2026年8月6日(木) 15:00 - 16:00
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)
Cooperation among unrelated individuals is a central puzzle in the evolution of social behavior. Indirect reciprocity offers one influential explanation: people help others not only because they expect direct returns, but also because their actions affect their reputation. In this seminar, I will review mathematical theories of indirect reciprocity, focusing on how reputation and social norms can sustain cooperation. I will begin with the classical framework of public assessment, where everyone shares the same view of each individual’s reputation, including the seminal work of Ohtsuki and Iwasa on the “leading eight” social norms. I will then turn to private assessment, where individuals may disagree about others’ reputations, and discuss why synchronization of opinions becomes essential for cooperation. Overall, the seminar aims to provide an accessible overview of how mathematical models allow us to formalize moral judgments—what counts as good or bad behavior—and to understand the evolution of cooperation through reputation.
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 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
13 イベント