Migration is a key driver of economic and societal transformation, touching people’s lives worldwide. Understanding why people decide to migrate is crucial for fostering inclusive and diverse societies and informing effective policy-making. This paper focuses on economic migrants, a particular group of migrants whose study has primarily been confined to narrow areas of interest and characterized by inconsistent terminology, limiting cross-study comparability and the synthesis of findings. Viewed through the interdisciplinary lens and derived from theoretical, empirical, and analytical research outcomes, the present paper concludes that economic migrants’ movements are influenced by the socio-demographic factors of ‘age’ and ‘education’ and are motivated by both the economic motives of ‘expected income’ and ‘employment’ and the economic-related motives of ‘corruption’, ‘amenities’, and ‘happiness’. These motives also reveal a typical profile of economic migrants: working-age, highly educated, predominantly male individuals who seek opportunities in developed countries to achieve a fulfilling life. The presented findings contribute to a better understanding of the dynamic decision-making process of economic migrants and offer valuable insights for policymakers to design more nuanced and targeted strategies for integrating migrants into societies.

The current study explores the effects of a brief training program on complex problem solving (CPS) and dynamic decision making (DDM) performance in two computer-simulated tasks with different task characteristics, ChocoFine (N = 76) and WinFire (N = 99). Half of the participants in each simulation group received a brief training on 16 frequent CPS and DDM errors. We hypothesized that participants who received the training in errors would show better performance, report fewer errors, and show fewer behavioral errors compared to those who did not receive the error training prior to the start of the simulated tasks. The results showed that participants in both training groups had better performance scores. Participants in the training condition reported fewer self-reported errors compared to the no-training group only in the ChocoFine simulation. Regarding behavioral errors, status-quo bias was related to weaker performance in both simulations. These findings have implications for leaders who are prone for the status-quo bias and for organizations which could implement trainings programs for DDM and CPS.

Policy-makers use different decision-making strategies and base their decisions – more or less explicitly – on both expert knowledge and opinions in order to cope with the sheer complexity of societal challenges and the political environment. Most politicians rely to some extent on personal ideology in the implementation of public policies. Potential decision biases such as ‘repair service behavior’ – the human tendency to try fixing what appears to be most problematic at first – also influence decision-making. While ideology plays a prominent role in politics, we know too little about its effect on political decision-making. Some researchers would argue that the use of ideology and repair service behavior facilitate the decision process, while others suggest that it adversely affects the ability to make objective decisions. Using a political microworld simulation that reproduces complex real-world problems, we investigate the effects of repair service behavior and personal ideology on performance in a dynamic decision-making task. Although repair service behavior was not associated with performance, the results suggest that personal ideology significantly impaired goal attainment. Despite clear instructions to be as objective as possible and think critically in completing the task, ideology was powerful enough to disrupt objective policy-making, as indicated by the deviation from optimal scores and the overall microworld task goal, i.e., to win votes and be re-elected by the end of the game.

This contribution discusses competencies needed for regulating systems with properties of multi-causality and non-linear dynamics (therapeutic, economical, organizational, socio-political, technical, ecological, etc.). Various research communities have contributed insights, but none has come forward with an inclusive framework. To advance the debate, I propose to draw from dynamic systems theory (DST) and “4E” (embodied, embedded, enactive, and extended), cognition approaches, which offer a set of perspectives to understand what expert regulators in real-life settings do. They define the regulator's agency as skillfully imposing constraints on a target system and hereby creating context-sensitive openings for self-organizing dynamics, rather than “controlling” the system. Adept regulators apply multi-pronged and multi-timescale constraints to achieve nuanced effects. Among other things, their skill set includes scarcely noted enactive processual competencies for “emergence management”, which the intellectualistic and insufficiently ecologically situated accounts of the complex problem solving literature omit. To capture the nature of system regulation, I advocate treating regulation dynamics and target system dynamics “symmetrically” by grounding regulator competencies in concepts from complexity theory.

An Internet-based competitive marketing game, FIFA 20, served to investigate the effectiveness of two opposite strategies in soccer-player auctions under semi-naturalistic conditions. Granting the validity of both causal principles, the anchoring principle giving an advantage to starting with a high price (Ritov, 1996) and the traffic principle underlying the starting-low advantage (Ku, Galinsky, &Murnighan, 2006), we nevertheless expected starting low strategies to produce higher endprices under FIFA 20 conditions. Two experiments, each using multiple copies of two players from the lowest price segment (Kramaric, Pizzi) and from an elevated price segment (Laporte, Martial), corroborated these expectations. A starting-low advantage was evident in two utility aspects, enhanced average (profit) and reduced variance (uncertainty aversion) of end prices obtained for player copies offered at lower starting prices. However, when the causal impact of the manipulated starting value was overshadowed by extraneous media influences, these findings were reduced or disappeared but never reversed.

This paper reflects on Dietrich Dörner's observation that participants in complex dynamic control tasks exhibit a "tendency to economize", that is, they tend to minimize cognitive effort. I interpret this observation in terms of a dual processing approach and explore if the reluctance to adopt Type 2 processing could be rooted in biological energy saving. There is evidence that the energy available for the cortex at any point in time is quite limited. Therefore, effortful thinking comes at the cost of neglecting other cortical functions. The proposed dual processing approach to complex problem solving is explored in an experiment where we varied cognitive load by means of a secondary task in order to make Type 1 or Type 2 processing more likely. Results show that cognitive load had no effect on target achievement. Even in the single task condition, many participants prefer Type 1 processing, confirming Dörner's observation.