There are five different conceptual levels in computational social choice:

• Interpersonal Comparisons of Utility: One of the fundamental problems in economics is how to evaluate trade-offs across individuals. In the Trolly Problem, is it just to sacrifice one in favor of many? We cannot evaluate this trade-off without appealing to a greater ethic, such as utilitarianism. See Sen (1998) [Link].
• How do people know their own values? This is a self-valuation problem since individuals have to ask themselves how good some alternative is. In finance, prices are set by the relative value of goods and services with respect to each other. Still, value is inherently subjective, as food is worth more than gold in a deserted island.
• Communication and elicitation: If there are an exponential number of alternatives, such as in a combinatorial planning problem, how do people communicate their preferences efficiently?
• Truthful elicitation: Incentive compatibility. See the Gibbard-Satterthwaite Theorem.
• Aggregation: See Arrow’s Impossibility Theorem.

This post contains a handful of perspective papers in EconCS, sorted by topic.

Multi-Agent Systems

• Parsons, S., and Wooldridge, M. (2002). Game theory and decision theory in multi-agent systems. In AAMAS. [Link]
• Shoham, Y., Powers, R., & Grenager, T. (2007). If multi-agent learning is the answer, what is the question?. Artificial intelligence. [Link]

Pluralistic AI in the LLM-AGI age:

• Sorensen, T., Moore, J., Fisher, J., Gordon, M., Mireshghallah, N., Rytting, C. M., … & Choi, Y. (2024). A roadmap to pluralistic alignment. arXiv preprint arXiv:2402.05070.
• Conitzer, V., Freedman, R., Heitzig, J., Holliday, W. H., Jacobs, B. M., Lambert, N., … & Zwicker, W. S. (2024). Social choice for ai alignment: Dealing with diverse human feedback. CoRR.
• Hammond, L., Chan, A., Clifton, J., Hoelscher-Obermaier, J., Khan, A., McLean, E., … & Rahwan, I. (2025). Multi-agent risks from advanced ai. arXiv preprint arXiv:2502.14143.

Computational Social Choice

• Anshelevich, E., Filos-Ratsikas, A., Shah, N., and Voudouris, A. A. (2021). Distortion in social choice problems: The first 15 years and beyond. IJCAI. [Link]
• Faliszewski, P., and Procaccia, A. D. (2010). AI’s war on manipulation: Are we winning?. AI Magazine. [Link]
• Aziz, H. and Shah, N. (2020). Participatory Budgeting: Models and Approaches [Link]

Economics and Computation

• Sandholm, T. (2008). Computing in mechanism design. New Palgrave Dictionary of Economics. [Link]
• Parkes, D. C., and Wellman, M. P. (2015). Economic reasoning and artificial intelligence. Science. [Link]
• Einav, L., and Levin, J. (2014). Economics in the age of big data. Science. [Link]

Multi-Agent Reinforcement Learning

• Nowé, A., Vrancx, P., & De Hauwere, Y. M. (2012). Game theory and multi-agent reinforcement learning. Reinforcement Learning: State-of-the-Art. [Link]

Networks, Crowds, and Markets

• Kempe, D., Kleinberg, J., and Tardos, É. (2003). Maximizing the spread of influence through a social network. In Proceedings of SIGKDD. [Link]
• Easley, D., and Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge university press. [Link]
• Axelrod, R. (1997). The Dissemination of Culture: A Model with Local Convergence and Global Polarization. The Journal of Conflict Resolution. [Link]

Consumer Choice

• Gilboa, I., Postlewaite, A., and Schmeidler, D. (2021). The complexity of the consumer problem. Research in Economics. [Link]
• Hainmueller, J., Hopkins, D. J., & Yamamoto, T. (2014). Causal inference in conjoint analysis: Understanding multidimensional choices via stated preference experiments. Political analysis. [Link]

Computational Social Science

• Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A. L., Brewer, D., … and Van Alstyne, M. (2009). Computational social science. Science. [Link]
• Adamic, L. A., & Glance, N. (2005). The political blogosphere and the 2004 US election: divided they blog. In Proceedings of the 3rd international workshop on Link discovery. [Link]
• Pentland, A. (2014) Social Physics: How Good Ideas Spread-The Lessons from a New Science. [Amazon]

Cognitive Science

• Camerer, C. F., Ho, T. H., and Chong, J. K. (2004). A cognitive hierarchy model of games. The Quarterly Journal of Economics. [Link] (See also Keynes’s ‘Beauty Contest’.)
• Camerer, C. F. (2011). Behavioral game theory: Experiments in strategic interaction. Princeton university press. [Link]

Ethics and AI

• Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., … and Rahwan, I. (2018). The moral machine experiment. Nature. [Link]

Prediction, Learning, and Games

• Young, H. P. (1996). The economics of convention. Journal of economic perspectives. [Link]
• Fudenberg, D., & Levine, D. K. (1998). The theory of learning in games. MIT press. [Link]
• Young, H. P. (2004). Strategic learning and its limits. OUP Oxford. [Link]
• Cesa-Bianchi, N., & Lugosi, G. (2006). Prediction, learning, and games. Cambridge university press. [Link]
• Hart, S., and Mas-Colell, A. (2013). Simple adaptive strategies: from regret-matching to uncoupled dynamics. World Scientific. [Link]

AI Economics

• Horton, J. J. (2023). Large language models as simulated economic agents: What can we learn from homo silicus?. National Bureau of Economic Research. [Link]
• Zheng, S., Trott, A., Srinivasa, S., Naik, N., Gruesbeck, M., Parkes, D. C., and Socher, R. (2020). The AI economist: Improving equality and productivity with ai-driven tax policies. [Link]

TODO:

Note to self for adding these types of papers later:

• Behavioral social choice papers
• Beh econ experiments on ultimatum games (incl BO00/EC04)