Abstract

DECISIONS AND HOW THEY ARE MADE In the wider sense, decision making is embedded in the problem-solving process and its many stages (Davidson and Sternberg, 2003; Guss et al., 2010). In the narrow sense, decisionmaking is understood as the ability to select one of several alternatives and to act accordingly (Guss, 2004). Previous research has often focused on decision making in relatively predictable environmentswith clear goals (e.g., expected utility theory of von Neumann and Morgenstern, 1944). In recent decades the focus has been on decision making heuristics, i.e., strategies or rules of thumb, applied in uncertain situations (e.g., Tversky and Kahneman, 1974; Simon, 1979; Gigerenzer and Gaissmaier, 2011). Causality plays an important role in many cognitive processes – and causal cognition is itself influenced by culture (e.g., Norenzayan and Nisbett, 2000; Medin and Atran, 2004; Beller et al., 2009; Bender and Beller, 2011; for a controversial discussion of causal cognition, see Sperber et al., 1995). Causality is especially important during the decision-making process, because the decision maker has to predict what consequences specific decisions bring about before making a decision. Causality refers here to the predicted decision options, that a specific planned action, when executed under specific circumstances, will have a specific predicted effect. This definition of causality refers to Aristotle’s causa efficiens, i.e., an action is the origin and will cause an intended effect. Our understanding of causality is a constructivist understanding, because causality refers to the causal predictions of the actor and sometimes the actor’s predicted probability of causal consequences might differ from a normative-mathematical probability of causal consequences. Predictions by actor and mathematical probability might be quite high (“As it is raining slightly, I will use the big umbrella and therefore not get wet during my walk”), but predictions by actor might be high and mathematical probability might be quite low (“when I buy a lottery ticket and use the birthdates of my family as lucky numbers, then I will win a million dollars”). Thus one could speak of predicted causality guiding the decision-making process. We are referring here to the predictions of the actor across domains. The selection of decision alternatives is dependent on several factors such as importance, urgency, and likelihood of success (e.g., Dorner, 2008; Dorner and Guss, 2013). First, the predictions regarding decision alternatives involve the estimation of how important an alternative is. The importance is related to the human needs and the decision alternative, for example, to drink a glass of water when extremely thirsty would be more important than the decision alternative to call a friend to chat. Thus, although decision making is a cognitive process, it is related to our human needs and motivational processes. Second, predictions regarding decision alternatives involve estimations of time and resulting urgency. If I am in my office and it is 5:30 pm, and I want to buy some groceries for the weekend and I know the store closes at 6:00 pm, and I know it takes me 15 min to get to the store, then the decision alternative “check and respond to emails” is perceived as less urgent (if the time estimation to check and respond to emails is longer than a few minutes which is usually the case). Third, predictions regarding decision alternatives involve estimations of how likely it is that the predicted consequences actually happen. I know 15 min is the time I need to go to the store and I know I need an hour to check my emails and to respond to them. This predicted likelihood of success is dependent on one’s competence: first the epistemic competence, i.e., the fact knowledge and experiential knowledge of the past; and second, the general competence, i.e., an estimation of one’s ability to act successfully in the given situation (Dorner, 2008). High general competence is reflected in high predicted likelihood of success for decision alternatives (“I can do this”). In other words, one believes in oneself and that translates into one’s ability to deal with situations successfully. Judging importance, urgency, and likelihood of success for decision alternatives can occur either automatically or deliberately, i.e., unconsciously or consciously. Automatically means that based on previous experiences in similar situations, the predictions and their results are known and attributed to the current situation. Often certain cues in the current situation trigger the memory of similar situations and connected with those the successful actions in those situations which can then be applied in the current situation (e.g., recognition-primed decision making according to Klein, 2008). If the current situation is a novel situation, then deliberations about possible

Highlights

  • DECISIONS AND HOW THEY ARE MADE In the wider sense, decision making is embedded in the problem-solving process and its many stages (Davidson and Sternberg, 2003; Güss et al, 2010)

  • Our understanding of causality is a constructivist understanding, because causality refers to the causal predictions of the actor and sometimes the actor’s predicted probability of causal consequences might differ from a normative-mathematical probability of causal consequences

  • The importance is related to the human needs and the decision alternative, for example, to drink a glass of water when extremely thirsty would be more important than the decision alternative to call a friend to chat

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Summary

Introduction

DECISIONS AND HOW THEY ARE MADE In the wider sense, decision making is embedded in the problem-solving process and its many stages (Davidson and Sternberg, 2003; Güss et al, 2010). Causality is especially important during the decision-making process, because the decision maker has to predict what consequences specific decisions bring about before making a decision. The selection of decision alternatives is dependent on several factors such as importance, urgency, and likelihood of success (e.g., Dörner, 2008; Dörner and Güss, 2013).

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