Abstract
Option generation in decision-making research: why just talk?
Highlights
Kalis et al (2013) provide a lucid and useful discussion of options and option generation, and argue persuasively that these constructs can be instrumental in our understanding of decision making
We (Kolodny et al, in review, in preparation) propose that such a representation should take the form of a hierarchical directed graph, whose vertices represent significant units in various modalities—for example, objects, actions, or locations— and whose edges represent probabilistic associations among them. Such a representation lends itself in a straightforward manner to option generation: the agent can generate and run “internal simulations” by probabilistically following trajectories along the graph’s edges, each trajectory representing what the agent perceives as an optional sequence of events and actions in the world
Vertices in the graph are assigned value, providing necessary data for the decision-making process. This approach allows us to address the questions raised by Kalis et al.: how might constraints in the option generation process be applied? What can account for differences among individuals? It leads us to recognize other unknowns that may go unnoticed unless an algorithmic solution is sought
Summary
Kalis et al (2013) provide a lucid and useful discussion of options and option generation, and argue persuasively that these constructs can be instrumental in our understanding of decision making. As an illustration of the issue at hand, consider the definition by Kalis et al of options as “representations of candidates for goal-directed actions.” A model based on this definition must include an internal representation of actions and of goals, of the relevant aspects of the world with which the agent may interact, and these interactions’ predicted outcomes.
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