Satisficing Revisited

  • Authors:

  • Michael A. Goodrich;Wynn C. Stirling;Erwin R. Boer

  • Affiliations:

  • Computer Science Department, Brigham Young University, Provo, UT, USA Nissan Cambridge Basic Research, Nissan Research and Development, Inc., Cambridge, MA, USA;Electrical and Computer Engineering Department, Brigham Young University, Provo, UT, USA;Nissan Cambridge Basic Research, Nissan Research and Development, Inc., Cambridge, MA, USA

  • Venue:
  • Minds and Machines
  • Year:
  • 2000

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Abstract

In the debate between simple inference heuristics and complex decision mechanisms, we take a position squarely in the middle. A decision making process that extends to both naturalistic and novel settings should extend beyond the confines of this debate; both simple heuristics and complex mechanisms are cognitive skills adapted to and appropriate for some circumstances but not for others. Rather than ask `Which skill is better?' it is often more important to ask `When is a skill justified?' The selection and application of an appropriate cognitive skill for a particular problem has both costs and benefits, and therefore requires the resolution of a tradeoff. In revisiting satisficing, we observe that the essence of satisficing is tradeoff. Unlike heuristics, which derive their justification from empirical phenomena, and unlike optimal solutions, which derive their justification by an evaluation of alternatives, satisficing decision-making derives its justification by an evaluation of consequences. We formulate and present a satisficing decision paradigm that has its motivation in Herbert Simon's work on bounded rationality. We characterize satisficing using a cost–benefit tradeoff, and generate a decision rule applicable to both designing intelligent machines as well as describing human behavior.