Repeated causal decision making.

Abstract

Many of our decisions refer to actions that have a causal impact on the external environment. Such actions may not only allow for the mere learning of expected values or utilities but also for acquiring knowledge about the causal structure of our world. We used a repeated decision-making paradigm to examine what kind of knowledge people acquire in such situations and how they use their knowledge to adapt to changes in the decision context. Our studies show that decision makers' behavior is strongly contingent on their causal beliefs and that people exploit their causal knowledge to assess the consequences of changes in the decision problem. A high consistency between hypotheses about causal structure, causally expected values, and actual choices was observed. The experiments show that (a) existing causal hypotheses guide the interpretation of decision feedback, (b) consequences of decisions are used to revise existing causal beliefs, and (c) decision makers use the experienced feedback to induce a causal model of the choice situation even when they have no initial causal hypotheses, which (d) enables them to adapt their choices to changes of the decision problem. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Strategies to intervene on causal systems are adaptively selected

“…Again, the majority of interventions were made on high centrality nodes with many downstream effects, such as causes with multiple effects or root nodes of causal chains. A similar result was reported in a study by Hagmayer and Meder (2012) (see Table 1). While these patterns indicate the possible influence of PTS strategies, these papers did not explicitly model participants' intervention decisions.…”

Reasoning about causal relationships: Inferences on causal networks.

“…Hagmayer and Meder (2008; 2012, Meder & Hagmayer, 2009) investigated a similar phenomenon with the structures in Figure 20. The square nodes represent possible interventions, the P node represents an outcome to be maximized, and the plus signs denote the size of the outcome given that a given combination of nodes ( A , and or B , and or C ) is active.…”

“…The square nodes represent possible interventions, the P node represents an outcome to be maximized, and the plus signs denote the size of the outcome given that a given combination of nodes ( A , and or B , and or C ) is active. In Hagmayer and Meder’s study (2012; Experiment 3) participants first learned the causal structures (either Figure 20a or 20b) by activating L or W 100 times and observing whether A , B , or C became active and the value of P . Afterwards, participants were told that the A node was removed from the network, and they had 10 opportunities to activate L or W in order to maximize P .…”

The Moderating Effect of Organisational Structure on Information Quality and Decision-Making Effectiveness Link

“…Previous research shows that managers are able to learn to successfully manipulate and manage complex causal systems (Hagmayer, & Meder, 2013). This is because making a decision involves a metacognitive process that can allow individuals to exert cognitive control by enabling them to generate multiple, alternative decision frameworks that focus on interpreting, planning, and implementing goals (Haynie & Shepherd, 2009).…”