Using aspiration adaptation theory to improve learning
Avi Rosenfeld, Sarit Kraus · 2011
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.