Negative affective environments improve complex solving performance

Based on recent affect-cognition theories (Bless et al., 1996; Fiedler, 2001; Sinclair, 1988), the present study predicted and showed a differentiated influence of nice and nasty environments on complex problem solving (CPS). Environments were constructed by manipulating the target value ‘capital’ of a complex scenario: Participants in the nice environment (N=42) easily raised the capital and received positive feedback, whereas those in the nasty environment (N=42) hardly enhanced the capital and got negative feedback. The results showed that nasty environments increased negative and decreased positive affect. The reverse was true for nice environments. Furthermore, nasty environments influenced CPS by leading to a higher information retrieval and a better CPS performance. Surprisingly, the influence of environment on CPS was not mediated through affect (cf. Soldat & Sinclair, 2001), as recent affect-cognition theories suggest. The missing influence of affect and the strong impact of environment are discussed

Optimization as an analysis tool for human complex decision making

We present a problem class of mixed-integer nonlinear programs (MINLPs) with nonconvex continuous relaxations which stem from economic test scenarios that are used in the analysis of human complex problem solving. In a round-based scenario participants hold an executive function. A posteriori a performance indicator is calculated and correlated to personal measures such as intelligence, working memory, or emotion regulation. Altogether, we investigate 2088 optimization problems that differ in size and initial conditions, based on real-world experimental data from 12 rounds of 174 participants. The goals are twofold. First, from the optimal solutions we gain additional insight into a complex system, which facilitates the analysis of a participant’s performance in the test. Second, we propose a methodology to automatize this process by providing a new criterion based on the solution of a series of optimization problems. By providing a mathematical optimization model and this methodology, we disprove the assumption that the “fruit fly of complex problem solving,” the Tailorshop scenario that has been used for dozens of published studies, is not mathematically accessible—although it turns out to be extremely challenging even for advanced state-of-the-art global optimization algorithms and we were not able to solve all instances to global optimality in reasonable time in this study. The publicly available computational tool Tobago [TOBAGO web site https://sourceforge.net/projects/tobago] can be used to automatically generate problem instances of various complexity, contains interfaces to AMPL and GAMS, and is hence ideally suited as a testbed for different kinds of algorithms and solvers. Computational practice is reported with respect to the influence of integer variables, problem dimension, and local versus global optimization with different optimization codes