This post is based on a paper: “Does interaction matter? Testing whether a confidence heuristic can replace interaction in collective decision-making.” The authors are
Dan Bang, Riccardo Fusaroli, Kristian Tylén, Karsten Olsen, Peter E. Latham, Jennifer Y.F. Lau, Andreas Roepstorff, Geraint Rees, Chris D. Frith, and Bahador Bahrami. The paper appeared in Consciousness and Cognition 26 (2014) 13–23.
The paper indicates that there is a growing interest in the mechanisms underlying the ‘‘two-heads-better-than-one’’ (2HBT1) effect, which refers to the ability of dyads to make more accurate decisions than either of their members. Bahrami’s 2010 study, using a perceptual task in which two observers had to detect a visual target, showed that two heads become better than one by sharing their ‘confidence’ (i.e., an internal estimate of the probability of being correct), thus allowing them to identify who is more likely to be correct in a given situation. This tendency to evaluate the reliability of information by the confidence with which it is expressed has been termed the ‘confidence heuristic’. I do not recall having seen the acronym 2HBT1 before, but it does recall the post Dialectical Bootstrapping in which one forms his own dyad, Bootstrapping where one uses expert judgment, and Scott Page’s work Diversity or Systematic Error? However, this is the first discussion of a confidence heuristic.
This post is based on the paper: “Multi-attribute utility models as cognitive search engines”, by Pantelis P. Analytis, Amit Kothiyal, and Konstantinos Katsikopoulos that appeared in Judgment and Decision Making, Vol. 9, No. 5, September 2014, pp. 403–419. This post does not look at persistence (post Persistence or delay (post Decision Delay) when you believe that you need more alternatives, but when to quit your search and stop within the available alternatives.
In optimal stopping problems, decision makers are assumed to search randomly to learn the utility of alternatives; in contrast, in one-shot multi-attribute utility optimization, decision makers are assumed to have perfect knowledge of utilities. The authors point out that these two contexts represent the boundaries of a continuum, of which the middle remains uncharted: How should people search intelligently when they possess imperfect information about the alternatives? They pose the example of trying to hire a new employee faced with several dozen applications listing their skills and credentials. You need interviews to determine each candidate’s potential. What is the best way to organize the interview process? First, you need to decide the order in which you will be inviting candidates. Then, after each interview you need to decide whether to make an offer to one of the interviewed candidates, thus stopping your search. The first problem is an ordering problem and the second a stopping problem. If credentials were adequate, you would not need an interview, and if credentials were worthless, you would invite people for interviews randomly.
Having the good fortune to be lost in Venice, I was reminded of the nuances of the recognition heuristic. My wife found the perfect antique store which was unsurprisingly closed for lunch. We went on, but a couple of hours later we tried to recreate our steps. For much of the journey, we did well having only to recognize that we had seen a particular store or archway or bridge before. Unfortunately, that broke down when we realized that we were retracing our steps in a five minute period. We still remembered that walk to the restaurant the first night, but it was unfortunately not differentiated in our minds. This was certainly an example of less knowledge being more. Eventually, using a combination of GPS and maps we found our way back to our hotel, but we never did find that antique store. And I was trying.
This post is based on a paper by Benjamin Hilbig, Martha Michalkiewicz, Marta Castela, Rudiger Pohl and Edgar Erdfelder: “Whatever the cost? Information integration in memory-based inferences depends on cognitive effort.” that was scheduled to appear in Memory and Cognition 2014. Fundamental uncertainty is a not uncommon situation for our decision making. There is an ongoing argument with the fast and frugal heuristics toolbox approach and the single tool approaches of evidence accumulation and parallel constraint satisfaction. However that argument depends on the particular task, the type of task, and on and on. I am still waiting for a giant table that puts all those things together.