Tootfinder

The Emergence of Altruism in Large-Language-Model Agents Society
Haoyang Li, Xiao Jia, Zhanzhan Zhao
https://arxiv.org/abs/2509.22537 https://arxiv.org/pdf…

The Emergence of Altruism in Large-Language-Model Agents Society
Leveraging Large Language Models (LLMs) for social simulation is a frontier in computational social science. Understanding the social logics these agents embody is critical to this attempt. However, existing research has primarily focused on cooperation in small-scale, task-oriented games, overlooking how altruism, which means sacrificing self-interest for collective benefit, emerges in large-scale agent societies. To address this gap, we introduce a Schelling-variant urban migration model that…

Space, time and altruism in pandemics and the climate emergency
Chris T. Bauch, Athira Satheesh Kumar, Kamal Jnawali, Karoline Wiesner, Simon A. Levin, Madhur Anand
https://arxiv.org/abs/2510.06236

Space, time and altruism in pandemics and the climate emergency
Climate change is a global emergency, as was the COVID-19 pandemic. Why was our collective response to COVID-19 so much stronger than our response to the climate emergency, to date? We hypothesize that the answer has to do with the scale of the systems, and not just spatial and temporal scales but also the `altruistic scale' that measures whether an action must rely upon altruistic motives for it to be adopted. We treat COVID-19 and climate change as common pool resource problems that exemplify…

Inference of Intrinsic Rewards and Fairness in Multi-Agent Systems
Victor Villin, Christos Dimitrakakis
https://arxiv.org/abs/2509.07650 https://arxiv.org/…

Inference of Intrinsic Rewards and Fairness in Multi-Agent Systems
From altruism to antagonism, fairness plays a central role in social interactions. But can we truly understand how fair someone is, especially without explicit knowledge of their preferences? We cast this challenge as a multi-agent inverse reinforcement learning problem, explicitly structuring rewards to reflect how agents value the welfare of others. We introduce novel Bayesian strategies, reasoning about the optimality of demonstrations and characterisation of equilibria in general-sum Markov…

The Paradox of Doom: Acknowledging Extinction Risk Reduces the Incentive to Prevent It
Jakub Growiec, Klaus Prettner
https://arxiv.org/abs/2509.04855 https://

The Paradox of Doom: Acknowledging Extinction Risk Reduces the Incentive to Prevent It
We investigate the salience of extinction risk as a source of impatience. Our framework distinguishes between human extinction risk and individual mortality risk while allowing for various degrees of intergenerational altruism. Additionally, we consider the evolutionarily motivated "selfish gene" perspective. We find that the risk of human extinction is an indispensable component of the discount rate, whereas individual mortality risk can be hedged against - partially or fully, depending on the…

Crosslisted article(s) found for q-bio.PE. https://arxiv.org/list/q-bio.PE/new
[1/1]:
- Space, time and altruism in pandemics and the climate emergency
Chris T. Bauch, Athira Satheesh Kumar, Kamal Jnawali, Karoline Wiesner, Simon A. Levin, Madhur Anand

An LLM-based Agent Simulation Approach to Study Moral Evolution
Zhou Ziheng, Huacong Tang, Mingjie Bi, Yipeng Kang, Wanying He, Fang Sun, Yizhou Sun, Ying Nian Wu, Demetri Terzopoulos, Fangwei Zhong
https://arxiv.org/abs/2509.17703

An LLM-based Agent Simulation Approach to Study Moral Evolution
The evolution of morality presents a puzzle: natural selection should favor self-interest, yet humans developed moral systems promoting altruism. We address this question by introducing a novel Large Language Model (LLM)-based agent simulation framework modeling prehistoric hunter-gatherer societies. This platform is designed to probe diverse questions in social evolution, from survival advantages to inter-group dynamics. To investigate moral evolution, we designed agents with varying moral dis…