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Ambiguity-Reduction: a Satisficing Criterion for Decision Making Giovanni Pezzulo (giovanni.pezzulo@istc.cnr.it) Institute of Cognitive Science and Technology - CNR Via S. Martino della Battaglia, 44 - 00185 Roma, Italy Alessandro Couyoumdjian (alessandro.couyoumdjian@uniroma1.it) University of Rome “La Sapienza” Piazzale Aldo Moro, 9 - 00185 Roma, Italy metacognitive dynamics during decision making. In the sec- ond Section we present two experiments using the Two Cards Gambling Game (TCGG) paradigm, permitting to study how information is integrated and exploited under uncertainty: participants can bet on cards shuffled at different velocities, knowing the bet of other Gamblers having variable reliability. Abstract In the domain of decision making under uncertainty we pro- pose the Multiple Source Evaluation Model, focusing on (1) how information coming from different evidential sources, ei- ther converging or diverging, is integrated and (2) how Ig- norance, Uncertainty and Contradiction are evaluated and re- duced before deciding in ambiguous domains. We argue that these operations involve an unique satisficing strategy, ambiguity-reduction. We introduce the Two Cards Gambling Game experimental paradigm and we present two experiments for exploring the main empirical implications of the model. The Multiple Source Evaluation Model In the judgment and decision making domain several com- putational models have been recently developed (for a re- cent review see Busemeyer and Johnson (2004)). Among the most influential ones, there are decision field theory (DFT, Busemeyer and Townsend (1993)) and the Leaky, Competing Accumulator Model (LCAM, Usher and McClelland (2001)). Briefly, DFT is a connectionist model based on psychologi- cal rather than economic principles. It consists of a three layer neural network. The connections, linking the inputs (affective evaluations of the possible consequences of a decision) to the first layer represent an attentional weighting process, that se- lects the options processed at present time. Differently, the connections between first and second layers perform compar- isons among weighted values of the options; the outputs of the second layer are the valences, that represent the advan- tages/disadvantages of each option over the other options. Fi- nally, the connections, between the second and third layers, and the interconnections in the third layer (lateral inhibition), by integrating valences over time, produce a preference state for each option. When one of the preference states reaches a threshold bound, the choice and the deliberation time of a decision are determined. Even if, in such a way, DFT can simulate various psy- chological effects (i.e. loss aversion, preference reversal), it, as most of the other computational models, does not explic- itly take into account some aspects of cognition that usually influence behaviour: metacognition and background knowl- edge. Here, we introduce a model of how different sources of information are evaluated and integrated in decision mak- ing contexts, extending (Castelfranchi, 1996; Pezzulo, Lorini, & Calvi, 2004). The fundamental claim is that the result of a decision depends on the supporting epistemic struc- ture,composed of: (1) beliefs in the domain of decision (base beliefs about information sources and their reliability); (2) beliefs about the domain of decision (the meta-beliefs: eval- uation of ambiguity in the current decision); (3) background knowledge (declarative and procedural expertise in the do- main of decision). Beliefs in the domain depend mainly on evidences provided by information sources, weighed according to their reliability. These beliefs are also used at the metalevel for evaluating the Keywords: decision making, uncertainty, ambiguity, heuristic Introduction Which strategies do individuals use for deciding in ambigu- ous and uncertain situations? In the fifties Herbert Simon introduced the term “satisficing” (Simon, 1957), a combina- tion of the words “satisfying” and “sufficing”. A satisficing solution is a sub-optimal one, i.e. allowing reasonable ap- proximation of the complexity of reality within given con- straints, one that is “good enough” as a basis for deciding. This means that people do not only consider the knowledge in the domain, but also have some prior knowledge and exper- tise about how to reason within the domain, including which sources to consider, what strategy to adopt and when to stop reasoning and decide. We assume that the procedural exper- tise of decision making in a domain consists of applying a set of rules, called here epistemic actions, which aim mainly at strengthening a belief structure before deciding, for example by reducing uncertainty and ignorance. Some good examples of epistemic actions are the fast and frugal heuristics such as “Take The Best” introduced by Gigerenzer and Todd (1999), but also strategies such as “ask for new information” or “re- vise reliability of a source”. We will argue in favor of an unique satisficing principle: individuals tend to select the epistemic action resulting in a more stable basis for deciding in order to reduce ambiguity to an acceptable degree. This criterium needs a limited amount of knowledge and processing time, without pre-calculating them, according to the desiderata of the “bounded rationality” research program (Gigerenzer & Todd, 1999). Consistently, we will also claim that many coarse-grained heuristics such as representativeness and availability (Kahneman, 2003) are phenomena which emerge from this more fine-grained strat- egy, depending on contextual factors. In the first Section we propose a model of decision under uncertainty, the Multiple Source Evaluation Model (MSEM), providing a comprehensive account of how decision is based on beliefs and belief sources and which are the most relevant
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