Using Brunswik’s (1952) lens model framework, Hammond (1965) proposed interpersonal conflict theory to explain the nature, source, and resolution of disagreement or “cognitive conflict” between parties performing judgment tasks. An early review by Brehmer (1976) highlighted the potential of this approach in, for example, understanding the structure of cognitive conflicts, and the effect of task and person variables on judgment policy change and conflict resolution. However, our bibliographic and content reviews from 1976 to the present day demonstrate that research on cognitive conflict using the lens model has declined sharply, while research on “task conflict” has grown dramatically. There has also been a shift to less theoretical precision and methodological rigor. We discuss possible reasons for these developments, and suggest ways in which lens model research on cognitive conflict can be revitalized by borrowing from recent theoretical and methodological advances in the field of judgment and decision making.

The collective recognition heuristic is a simple forecasting heuristic that bets on the fact that people’s recognition knowledge of names is a proxy for their competitiveness: In sports, it predicts that the better-known team or player wins a game. We present two studies on the predictive power of recognition in forecasting soccer games (World Cup 2006 and UEFA Euro 2008) and analyze previously published results. The performance of the collective recognition heuristic is compared to two benchmarks: predictions based on official rankings and aggregated betting odds. Across three soccer and two tennis tournaments, the predictions based on recognition performed similar to those based on rankings; when compared with betting odds, the heuristic fared reasonably well. Forecasts based on rankings—but not on betting odds—were improved by incorporating collective recognition information. We discuss the use of recognition for forecasting in sports and conclude that aggregating across individual ignorance spawns collective wisdom.

Contrary to the common belief that more information is always better, Gigerenzer et al. (1999) showed that simple decision strategies which rely on little information can be quite successful. The success of simple strategies depends both on bets about the structure of the environment and on the core capacities of the human mind, such as recognition memory (Gigerenzer, 2004). However, the interplay between the environment and the mind’s core capacities has rarely been precisely modeled. We illustrate how these environment-mind interactions could be formally modeled within the cognitive architecture ACT-R (J. R. Anderson et al., 2004). ACT-R is an integrated theory of mind that is tuned to the statistical structure of the environment, and it can account for a variety of phenomena such as learning, problem solving, and decision making. Here, we focus on studying decision strategies and show how the success of theses strategies in particular environments depends on characteristics of core cognitive capacities, such as recognition and short term memory.

Professional judges in traffic courts sentence many hundreds of offenders per year. Using 639 case files from archives, we compared the Matching Heuristic (MH) to compensatory, weighing algorithms (WM). We modeled and cross validated the models on different subsets of the data, and took several other methodological precautions such as allowing each model to select the optimal number of variables and ordering and weighing the variables in accordance to different logics. We did not reproduce the finding by Dhami (2003), who found the MH to be superior to a compensatory algorithm in modeling bail-granting decisions. These simulations brought out the inner logic of the two family of models, showing what combination of parameters works best. It remains remarkable that using only a fraction of the variables and combining them non-compensatorily, MH obtained nearly as good a fit as the weighing method.