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Commit 367fc0eb authored by Maxime MORGE's avatar Maxime MORGE
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LLM4AAMAS: Add a Game Theory Section

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...@@ -298,28 +298,6 @@ master, a designer or an analyst. ...@@ -298,28 +298,6 @@ master, a designer or an analyst.
Todd, Marvin Zammit, Sam Earle, Antonios Liapis, Julian Togelius, Georgios N. Todd, Marvin Zammit, Sam Earle, Antonios Liapis, Julian Togelius, Georgios N.
Yannakakis (2024) Published in *IEEE Transactions on Games* Yannakakis (2024) Published in *IEEE Transactions on Games*
LLMs have the ability to emulate a real human in certain experiments in
experimental economics or social psychology.
- **[Large language models as simulated economic agents: What can we learn from
homo silicus?](https://www.nber.org/papers/w31122)** Horton, J. J. (2023).
National Bureau of Economic Research.
LLMs, notably GPT-4 using ToT prompt, can simulate simple auction experiments in
line with theoretical expectations
- *[The nuances of large-language-model-agent performance in simple English
auctions](https://www.academia.edu/download/112356998/13_231004_2023_Jan_Reg_Nuances_of_LLM_Performance_English_Auctions_Parady_USA_Published.pdf)**
Lamichhane, B., Palardy, J., & Singh, A. K. (2023). Empirical Economics
Letters,2(1).
Generative consultants as economic agent with limited agency.
- **[Generative AI as Economic
Agents](https://doi.org/10.1145/3699824.3699832)** Immorlica, N., Lucier,
B., & Slivkins, A. (2024). SIGecom Exch., 22(1), 93–109. ACM, New York, NY,
USA.
AGENTBENCH is a systematically designed multi-dimensional evolving benchmark for AGENTBENCH is a systematically designed multi-dimensional evolving benchmark for
evaluating LLMs as agents which measures a significant performance gap between evaluating LLMs as agents which measures a significant performance gap between
these top-tier models and their OSS competitors. these top-tier models and their OSS competitors.
...@@ -361,6 +339,8 @@ simulation. ...@@ -361,6 +339,8 @@ simulation.
Challenges](https://arxiv.org/abs/2402.01680)** Taicheng Guo et al. (2024) Challenges](https://arxiv.org/abs/2402.01680)** Taicheng Guo et al. (2024)
Published on *arXiv* arXiv:2402.01680 [cs.CL] Published on *arXiv* arXiv:2402.01680 [cs.CL]
### Social Simulation
LLMs can simulate realistic perceptions, reasoning, and decision-making, react LLMs can simulate realistic perceptions, reasoning, and decision-making, react
adaptively to environments without predefined explicit instructions by adjusting adaptively to environments without predefined explicit instructions by adjusting
their responses through contextual learning mechanisms, autonomously generate their responses through contextual learning mechanisms, autonomously generate
...@@ -416,6 +396,32 @@ A simulation of the propagation processes in a social network. ...@@ -416,6 +396,32 @@ A simulation of the propagation processes in a social network.
Lu, Jinzhu Mao, Jinghua Piao, Huandong Wang, Depeng Jin, Yong Li (2023)* Lu, Jinzhu Mao, Jinghua Piao, Huandong Wang, Depeng Jin, Yong Li (2023)*
Published on *arXiv* arXiv:2307.14984 Published on *arXiv* arXiv:2307.14984
### Game Theory
LLMs have the ability to emulate a real human in certain experiments in
experimental economics or social psychology.
- **[Large language models as simulated economic agents: What can we learn from
homo silicus?](https://www.nber.org/papers/w31122)** Horton, J. J. (2023).
National Bureau of Economic Research.
LLMs, notably GPT-4 using ToT prompt, can simulate simple auction experiments in
line with theoretical expectations.
- *[The nuances of large-language-model-agent performance in simple English
auctions](https://www.academia.edu/download/112356998/13_231004_2023_Jan_Reg_Nuances_of_LLM_Performance_English_Auctions_Parady_USA_Published.pdf)**
Lamichhane, B., Palardy, J., & Singh, A. K. (2023). Empirical Economics
Letters,2(1).
Generative consultants as economic agent with limited agency.
- **[Generative AI as Economic
Agents](https://doi.org/10.1145/3699824.3699832)** Immorlica, N., Lucier,
B., & Slivkins, A. (2024). SIGecom Exch., 22(1), 93–109. ACM, New York, NY,
USA.
This paper assesses the economic rationality of GPT's decisions across four This paper assesses the economic rationality of GPT's decisions across four
domains: risk, time, social, and food preferences. The experiments reveal that domains: risk, time, social, and food preferences. The experiments reveal that
GPT's decisions exhibit greater rationality than those of humans. This GPT's decisions exhibit greater rationality than those of humans. This
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