Modeling Individual Decisions from Information Search

Modeling Individual Decisions from Information Search

Neha Sharma (School of Computing and Electrical Engineering, Indian Institute of Technology, Mandi, India) and Varun Dutt (School of Computing and Electrical Engineering, School of Humanities and Social Sciences, Indian Institute of Technology, Mandi, India)
Copyright: © 2015 |Pages: 12
DOI: 10.4018/978-1-4666-5888-2.ch455
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Background

In order to study people’s search and choice behaviors in the laboratory, a “sampling paradigm” has been proposed in the DFE research (Hertwig & Erev, 2009). In the sampling paradigm, people are presented with two or more options to choose between. These options are represented as blank buttons on a computer screen. People are first asked to sample as many outcomes as they wish from different button options (information search). Once people are satisfied with their sampling of the options, they decide from which option to make a single final choice for real.

In the sampling paradigm, two classes of models have been proposed and these classes include the associative learning models and cognitive heuristics (Hertwig, 2011). Among the associative learning class, human choice is conceptualized as a learning process (for example, Busemeyer & Myung, 1992; Bush & Mosteller, 1955). Learning consists in changing the propensity to select a gamble based on the experienced outcomes. Good experiences boost the propensity of choosing the gamble associated with them, and bad experiences diminish it (e.g., Barron & Erev, 2003; Denrell, 2007; Erev & Barron, 2005; March, 1996). Some of the models in this class include the Instance-Based Learning (IBL) model (Gonzalez & Dutt, 2011; Lejarraga, Dutt, & Gonzalez, 2012), value-updating model (Hertwig et al., 2006), and fractional adjustment model (March, 1996). Among all the models in the associative class, the IBL model has been shown as the best performing model at the aggregate level (Gonzalez & Dutt, 2011, 2012). Thus, we choose the IBL model as a first model for evaluation in this article.

Key Terms in this Chapter

Final Choice: The act of choosing one option for real, where the cost or benefit to a participant is determined by the payoff obtained.

Sampling: The act of choosing options without any cost or benefit to a participant.

Computational Cognitive Models: Mathematical models developed to understand human decision making, where these models use cognitive assumptions like imperfect memory and noise.

Observation: A participant playing a problem in a dataset.

Experience: Amount of sampled information that each participant has gained based upon the participant’s sampling of options.

Individual Choice: Choice made by a participant for an option in a problem.

Error Ratio: Ratio of the number of correctly predicted human choices by a model divided by the total number of observations.

Aggregate Choice: Proportion of choices for an option in a problem aggregated across all participants playing that problem.

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