Effectively modeling an agent’s cognitive model is an important problem in many domains. In this paper, we explore the agents people wrote to operate within optimization problems. We claim that the overwhelming majority of these agents used strategies based on bounded rationality, even when optimal solutions could have been implemented. Particularly, we believe that many elements from Aspiration Adaptation Theory (AAT) are useful in quantifying these strategies. To support these claims, we present extensive empirical results from over a hundred agents programmed to perform in optimization problems involving solving for one and two variables. 1

Creating agents that properly simulate and interact with people is critical for many applications. Towards creating these agents, models are needed that quickly and accurately predict how people behave in a variety of domains and problems. This paper explores how one bounded rationality theory, Aspiration Adaptation Theory (AAT), can be used to aid in this task. We extensively studied two types of problems – a relatively simple optimization problem and two complex negotiation problems. We compared the predictive capabilities of traditional learning methods with those where we added key elements of AAT and other optimal and bounded rationality models. Within the extensive empirical studies we conducted, we found that machine learning models combined with AAT were most effective in quickly and accurately predicting people’s behavior.

The design of automated negotiators has been the focus of abundant research in recent years. However, due to difficulties involved in creating generalized agents that can negotiate in several domains and against human counterparts, many automated negotiators are domain specific and their behavior cannot be generalized for other domains. Some of these difficulties arise from the differences inherent within the domains, the need to understand and learn negotiators ’ diverse preferences concerning issues of the domain and the different strategies negotiators can undertake. In this paper we present a system that enables alleviation of the difficulties in the design process of general automated negotiators termed GENIUS, a General Environment for Negotiation with Intelligent multipurpose Usage Simulation. With the constant introduction of new domains, e-commerce and other applications, which require automated negotiations, generic automated negotiators encompass many benefits and advantages over agents that are designed for a specific domain. Based on experiments conducted with automated agents designed by human subjects using GENIUS we provide both quantitative and qualitative results to illustrate its efficacy. Finally, we also analyze a recent automated bilateral negotiators competition that was based on GENIUS. Our results show the advantages and underlying benefits of using GENIUS and how it can facilitate the design of general automated negotiators.

The design of automated negotiators has been the focus of abundant research in recent years. However, due to difficulties involved in creating generalized agents that can negotiate in several domains and against human counterparts, many automated negotiators are domain specific and their behavior cannot be generalized for other domains. Some of these difficulties arise from the differences inherent within the domains, the need to understand and learn negotiators’ diverse preferences concerning issues of the domain and the different strategies negotiators can undertake. In this paper we present a system that enables alleviation of the difficulties in the design process of general automated negotiators termed GENIUS, a General Environment for Negotiation with Intelligent multi-purpose Usage Simulation. With the constant introduction of new domains, e-commerce and other applications, which require automated negotiations, generic automated negotiators encompass many benefits and advantages over agents that are designed for a specific domain. Based on experiments conducted with automated agents designed by human subjects using GENIUS we provide both quantitative and qualitative results to illustrate its efficacy. Our results show the advantages and underlying benefits of using GENIUS for designing general automated negotiators. 1.

Creating agents that proficiently interact with people is critical for many applications. Towards creating these agents, models are needed that effectively predict people’s decisions in a variety of problems. To date, two approaches have been suggested to generally describe people’s decision behavior. One approach creates a-priori predictions about people’s behavior, either based on theoretical rational behavior or based on psychological models, including bounded rationality. A second type of approach focuses on creating models based exclusively on observations of people’s behavior. At the forefront of these types of methods are various machine learning algorithms. This paper explores how these two approaches can be compared and combined in different types of domains. In relatively simple domains, both psychological models and machine learning yield clear prediction models with nearly identical results. In more complex domains, the exact action predicted by psychological models is not even clear, and machine learning models are even less accurate. Nonetheless, we present a novel approach of creating hybrid methods that incorporate features from psychological models in conjunction with machine learning in order to create significantly improved models for predicting people’s decisions. To demonstrate these claims, we

Abstract Creating agents that realistically simulate and interact with people is an important problem. In this paper we present strong empirical evidence that such agents should be based on bounded rationality, and specifically on key elements from Aspiration Adaptation Theory (AAT). First, we analyzed the strategies people described they would use to solve two relatively basic optimization problems involving one and two parameters. Second, we studied the agents a different group of people wrote to solve these same problems. We then studied two realistic negotiation problems involving five and six parameters. Again, first we studied the negotiation strategies people used when interacting with other people. Then we studied two state of the art automated negotiation agents and negotiation sessions between these agents and people. We found that in both the optimizing and negotiation problems the overwhelming majority of automated agents and people used key elements from AAT, even when optimal solutions, machine learning techniques for solving multiple parameters, or bounded techniques other than AAT could have been implemented. We discuss the implications of our findings including suggestions for designing more effective agents for game and simulation environments. We would like to acknowledge the many productive conversations and visits with Prof. Reinhard Selten, Dr. Martin Hohnisch, and Sabine Pittnauer of the University of Bonn. We would also like to thank the anonymous reviewers for their comments, as well as Yael Ejgenberg, Ron Adany, Yosi Ben-Agu, Roni Toledano, Anat Sevet, and Yael Blumberg for judging the agents in Section 4. This work was supported in part by the National