Parallel Constraint Satisfaction Theory

pcsfox Parallel Constraint Satisfaction Theory (this was discussed to some extent in the post “Intuition in J/DM“) is a descriptive theory of decision making whose main proponents are Andreas Glockner and Tilman Betsch. They propose that decision making uses analytic processes for information search and production and intuitive (automatic) processes for combining information and making the decisions. To provide this basic outline of PCS, I am using the preprints “Experts and Decision Making: First Steps Towards a Unifying Theory of Decision Making in Novices, Intermediates and Experts,” by Britta Herbig / Andreas Glöckner and published by the Max Planck Institute for Research on Collective Goods, Bonn 2009/2, and “How Evolution Outwits Bounded Rationality The Efficient Interaction of Automatic and Deliberate Processes in Decision Making and Implications for Institutions” by Andreas Glockner and published by the Max Planck Institute for Research on Collective Goods, Bonn 2008/8.  I will likely be looking at specific parts of this theory in future posts. The PCS model of decision making assumes a primacy of intuitive (automatic) processes and gives analytic processes (only) a supporting part for the optimization of decisions (see Figure A). According to the PCS model, every decision starts with the perception of a decision problem. This leads to an automatic activation of a multitude of associated information that forms a temporally activated network (see Figure 1). Once this primary network is constructed, automatic consistency-maximizing processes operate towards establishing a consistent interpretation of activated information. In this way, different ways of interpreting the decision situation are weighed against each other, and the most probable interpretation is highlighted. This happens by way of a devaluation of information that does not support the dominant interpretation, and a simultaneous increase in the valuation of information that does. The process results in a mental representation that guides decision making. In many everyday situations, and  this proceeds completely automatically:  a situation is perceived, a completely unconscious mental representation (primary network) is constructed and the favored option is chosen (see Figure A). This process parallels the formation of a good “Gestalt” in perception. Figure A vaseindex For example, when presented with an image of changing figure-ground relationships, individuals perceive either a vase or two faces on the basis of exactly the same  information. By shifting the focus of attention, perception may flip to the opposite interpretation. Conceptually, a myriad of conflicting information is unconsciously integrated in one consistent interpretation (e.g., vase). In this process, the interpretation of information is modified. Information that speaks against the dominant interpretation  (e.g., an object that shades a part of the figure) is suppressed, whereas information that supports the dominant interpretation (e.g., a characteristic shape) is accentuated. The PCS approach to decision making is based on the same principle. As soon as individuals are confronted with a decision task, automatic processes are initiated which work to form a consistent mental representation of the task. In the process, information supporting the emerging mental representation is accepted while conflicting information is devaluated. Conceptually, automatic processes weigh interpretations of information against each other by taking into account the complex constellation of the information. The best interpretation wins the competition, and conflicting information is eliminated as far as possible. Individuals are not aware of these processes; they are only aware of the results. In contrast to other animals, humans have developed the ability to supervise and manipulate deliberately the powerful but inflexible automatic processes of the primary network. Glöckner and Betsch assume that the relations between elements in the primary network (intuitive) are determined mainly by slow learning processes. Thus, changes in these relations usually take a long time, and quick adaptations to environmental change are impossible. The evolutionary advantage of the additional deliberate system  (secondary network) is that it facilitates faster behavioral adaptations and allows for directed information search, qualified information production, and simulations to find the global maximum of consistency. Although the deliberate processes are rather limited in their computational capacity, they allow for better and faster adaptations by providing further information and temporarily changing the network so as to find quickly a consistent solution. However, without the automatic system, the deliberate system would be computationally overloaded on a chronic basis. Can the PCS Rule Account for Biases in Decision Making?   According to the PCS rule, all deviations from optimal decisions are essentially caused by the fact that the mental representation of the decision task (i.e., the primary network) represents the real structure of the decision task inaccurately. Thus, in contrast to the heuristics and biases program, the PCS rule does not assume that different heuristics lead to certain biases but that one single mechanism accounts for all of them. Systematic misperceptions can be caused by all of the factors that have been repeatedly discussed in the literature, such as framing, anchoring, salience, status quo, mental accounting. In my mind, PCS seems like Fuzzy Trace Theory in that coherence is important. The multiple gists are “added up” and if there is coherence of the gists, the deliberative (verbatim) system is not even really turned on.  If there is no consistency or coherence, then the deliberative (analytic) system is asked for input or to search for more information.  I am hoping to find some discussion of the two theories in one place so I can better put them in perspective or see if they could be integrated. IPDAS, the International Patient Decision Aids Standards Collaboration does examine decision theories as a way to design value clarification methods. Its analysis concludes that PCS can help identify options, identify attributes of situations and/or options, while Fuzzy Trace Theory provides reasoning about options or attributes of options, makes holistic comparisons, and helps retrieve relevant values.  Both help to integrate attributes of options.

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