LLM4AAMAS: Add a Game Theory Section

README.md

@@ -298,28 +298,6 @@ master, a designer or an analyst.
   Todd, Marvin Zammit, Sam Earle, Antonios Liapis, Julian Togelius, Georgios N.
   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
 evaluating LLMs as agents which measures a significant performance gap between
 these top-tier models and their OSS competitors.
@@ -361,6 +339,8 @@ simulation.
   Challenges](https://arxiv.org/abs/2402.01680)** Taicheng Guo et al. (2024)
   Published on *arXiv* arXiv:2402.01680 [cs.CL]
 
+### Social Simulation
+
 LLMs can simulate realistic perceptions, reasoning, and decision-making, react
 adaptively to environments without predefined explicit instructions by adjusting
 their responses through contextual learning mechanisms, autonomously generate
@@ -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)*
   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
 domains: risk, time, social, and food preferences. The experiments reveal that
 GPT's decisions exhibit greater rationality than those of humans. This