Ris ky Decis ions : Active Ris k Management. Os wald Huber University of Fribourg, Switzerland. Manus cript accepted version 1

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1 Ris ky Decis ions : Active Ris k Management Os wald Huber University of Fribourg, Switzerland 1 1

2 Abs tract Decision behavior with realistic risky scenarios is quite different from that in choices between gambles: Decision makers are less interested in probability information. Instead, they often attempt to actively manipulate the risk in an otherwise attractive alternative: A ris k-defusing operator ( RDO) is an action intended by the decision maker to be performed in addition to a specific existing alternative, and it is expected to decrease the risk (e.g., vaccination, insurance). The search for an RDO and the incorporation of a detected RDO into the alternative cannot be modeled with classical decision theories or heuristics. The paper presents Risk Management Decision Theory that describes the decision process with and without RDOs, and gives an overview about experimental research with RDOs. The consequences of the RDO concept for theories of decision behaviour are discussed. Key words: decision making, risky decisions, risk defusing 2

3 Introduc tion Rose Flaherty is a manager in a small firm producing water-purification installations. She has planned to travel to the capital of Burtengo tomorrow in order to conclude a contract with the local administration, vitally important for her firm. Now she learns that unexpectedly an infectious dangerous disease rages in Burtengo. Thus, Flaherty is faced with a risky de cision. She has (at least) two alternative actions: To travel to Burtengo or to postpone the meeting. Each alternative leads to specific outcomes (consequences). For example, traveling to Burtengo has a desirable (positive) outcome: concluding the contract. However, it may result in an undesirable (negative) outcome: suffering from the dangerous disease. This negative outcome is uncertain. The existence of at least one uncertain (negative) outcome in at least one of the alternatives is a defining characteristic of risky decisions. When deciding, people assign subjective values to outcomes. They may also allocate degrees of uncertainty or probabilities to events and outcomes. Psychological decision theory investigates risky decision making experimentally with the help of the gambling- (lottery-) paradigm: A decision maker has to choose between (at least) two gambles as alternatives. A gamble consists of a set of outcomes (gains, losses) and their probabilities, all these components are known to the decision maker. Figure 1 presents typical examples. In such experiments, the central factors determining decision behavior are the subjective values (utilities) of the outcomes, and often -- their subjective probability. The most prominent decision theories founded on the gambling paradigm are the Subjectively Expected Utility theory ( S EU, see Fig. 1), and its descendants (e.g., Prospect theory, Kahneman & Tversky, 1979), Baron (2008) gives an overview. However, decision 3

4 Figure 1 : Gambling-paradigm GAMBLING-Paradig m: Sche me of alternatives Alternative a: o 1 with p(o 1 ), o 2 with p(o 2 ),, o n with p(o n ), o i... outcome o i, p(o i ) probability of outcome o i Example : GAMBLES Alternative x : gain of 7 with probability 0.4 loss of 4 with probability operationalized with wheel of Fortune 80 Alternative y: gain of 80 with probability 0.1 loss of 25 with probability 0.9 x - 4 y - 25 Example : SCENARIO pres tructured as gamble (adapted from Huber & Huber, 2009) You are the head of an international program to protect an endangered species of ocean turtles. The last of the remaining turtles are kept in a nature reserve. Unfortunately, the turtles do not breed in the reserve. Biologists have found two possible breeding places. One of them has to be chosen. Alte rnative Be ac h (non-risky alternative): with probability 1.00: low number of eggs produced and no danger for the eggs, Alte rnative Is land (risky alternative) with probability 0.4: high number of eggs and with probability 0.6: high number of eggs and no all eggs destroyed by parasites. eggs destroyed by parasites. DECIS ION RULES Subjectively expected utility (SEU) - model For each alternative a, the SEU is computed, according to the formula: SEU(a) = u(o 1 ) p(o 1 ) + u(o 2 ) p(o 2 ) + + u(o n ) p(o n ), where: u(o i ) subjective value of outcome o i p(o i ) subjective probability of outcome o i The alternative with the highest SEU is chosen Maximin - heuris tic For each alternative, the worst outcome is identified The alternative with the relatively bes t worst outcome is chosen 4

5 behavior is often explained better by heuristics than by such theories. Heuristics are strategies reducing the cognitive effort necessary to make a decision. The Maximinheuristic is as an example included in Figure 1. The predominance of the gambling-paradigm recently is challenged, however, by a number of experiments of our research group and of others, for example, Bär and Huber (2008), Huber and Huber (2008), Huber, Huber and Bär (2010), Tyszka and Zaleskiewicz (2006), Wilke, Haug and Funke (2008). Huber (2007) reviews the earlier results. In these experiments, realistic scenarios are presented instead of gambles and the alternatives are not prestructured like a gamble by the experimenter. These experiments demonstrate that results from research within the gambling paradigm cannot be generalized to all decision tasks. Decision behavior in scenarios differs in two respects from behavior in choices between gambles: 1. Most often, decision makers are content to know whether an outcome is possible or will occur with certainty. They are not actively interested in more precise probabilities. 2. Decision makers often search for an additional action (risk defusing operator) which eliminates the risk. A ris k-defus ing operator ( RDO) is an action anticipated by the decision maker to be performed in addition to an existing alternative and is expected to eliminate or decrease the risk. For example, the manager in the introductory example most likely will actively inquire whether a vaccination exists or whether an infection can be prevented (e.g., by killing the mosquitoes). All these additional actions are RDOs. Another example quite common in everyday risky situations is buying insurance. If an acceptable RDO is detected with a promising alternative, it is usually chosen. Different types of RDOs can be distinguished (cf., Huber, 2007), for example, those 5

6 preventing a negative outcome (e.g., vaccination) or others com pens ating for a negative outcome (e.g., insurance). In those experiments investigating the spontaneous use of probabilities or RDOs, most often the method of Active Information Search has been employed: Participants first are presented a short description of the alternatives, and then they can pose questions to the experimenter in order to obtain more information. Huber, Huber and Schulte-Mecklenbeck (2011) describe the method in detail and discuss its advantages and limitations. Shiloh, Gerad and Goldman (2006) applied the method in real life genetic counselling decisions and confirmed the general results. The findings obtained with the Active Information Search method are corroborated by Thinking aloud data (Bär & Huber, 2008). Furthermore, if decision makers have to justify their decision, RDOs take a prominent place in the justifications (Huber, Bär & Huber, 2009). The concept of RDOs is ignored in classical decision research. This neglect seems to follow from the assumption that all risky decisions are gambles. In gambles, there is usually no room for RDOs. Therefore, gambles represent only a subset of risky decisions. Incorporation o f RDOs into Alternatives The search for an RDO often gives rise to cost (money, time, effort ) and at the beginning it is not clear whether it will be successful or not. Decision makers are much more likely to undertake an RDO search for attractive alternatives than for less attractive ones (Huber et al., 2010). Also negative outcomes are looked for mainly for the attractive alternatives. Search is more likely if the expectation of success is higher (Huber & Huber, 2008). Context effects also are relevant: Search is more frequent under time pressure (Huber & Kunz, 2007), and when decision makers have 6

7 to justify their decision (Huber et al., 2009). Furthermore, the type of risk and the domain affect the search (Wilke, Haug & Funke, 2008). A detected RDO is not necessarily acceptable. The desirable effects of the RDO (prevention or compensation) have to be weighed against its costs (the premium of an insurance, time, undesirable side effects, etc.). The higher the cost of an RDO the less likely it is accepted (Williamson, Ranyard & Cuthbert, 2000). As mentioned above, most decision makers are satisfied with knowing whether an outcome is possible or will occur with certainty, and are not interested in more precise probabilities. This result contradicts one of the fundamental assumptions of classical decision theory. The following explanations for this finding do not exclude each other. The Role of Probabilitie s More precise probabilities are not necessary if the negative outcome of an alternative can be defused with an RDO. Decision makers do not expect to get reliable and valid probability information (for example, about the state of economy three years from now). Decision makers do not need more precise probabilities because they apply heuristics that do not require probabilities (e.g., Maximin). It should be noted that people do not generally recall or infer probabilities from their background knowledge (Huber & Macho, 2001; Bär & Huber, 2008). Furthermore, the little interest in probability is not an artifact of the Method of Active Information Search, because if gambles are presented, most decision makers do search for probabilities (Huber & Huber, 2008; Huber et al., 2010). 7

8 Start Comments Selection of promising alternative For all initially available alternatives, a rudimentary mental representation is constructed. This representation contains mainly the positive outcomes (if possible). The alternative with the best outcome is selected as promising alternative. (First part of the Advantages First Principle, Huber, Huber & Bär, 2010) Closer inspection of promising alternative Search for negative outcomes The prom is ing alternative is inspected in more detail and the representation is elaborated. The decision maker looks in particular for negative outcomes. (Second part of the Advantages First Principle, Huber, Huber & Bär, 2010) If negative outcome - RDO search? yes Acceptable RDO detected? no no If a negative outcome has been detected a local process decision has to be made (not necessarily consciously): whether or not to search for an RDO. The search decision is not required if an RDO comes into mind immediately. This local process decision is influenced by factors as described in the text. If an RDO has been found the decision maker has to decide whether it is acceptable (cost-benefit) as mentioned in the text. Detection of an acceptable RDO is an excellent predictor of choice, usually in more than 90% of cases the alternative with the RDO is chosen (see, e.g., Bär & Huber, 2008). yes Choice using heuristics: Maximin, Least Probable Choice of promising alternative Negative, Manus cript accepted version If the negative outcome cannot be defused, decision makers choose among the available (promising) alternatives with one of the heuristics, usually one that allows risk minimization passively, like Maximin. Which heuristic is used depends, e.g., on situational factors, frames, or personality characteristics. 8

9 Ris k Management Decis ion Theory (RMDT) In a nutshell, the model supposes that first the most promising alternative is selected from the set of all available ones, focusing primarily on positive outcomes. Then the decision maker minimizes the risk connected with the promising alternative, either actively with an RDO, or passively applying a heuristic like Maximin. Figure 2 describes the theory in more detail. The main characteristics of the theory can be summarized as follows: Active minimization of ris k. RDOs are active means to defuse risk. Classical decision theory offers a number of heuristics and models describing pas sive risk minimization (e.g., Maximin-heuristic, or Prospect-theory with adequate parameter values). However, none of these theories can model active risk minimization. Decis ion makers purs ue two g oals : Choosing an alternative that has attractive positive outcomes, and choosing an alternative that minimizes risk. At the beginning and with several alternatives, selecting the alternatives with most attractive positive outcomes is more important. If such promising alternatives have been identified, risk minimization becomes the centre of attention. Thus, most decision makers do not simply apply RDOs or, for example, Maximin from the beginning, but risk minimizing is restricted to the subset of promising alternatives. Interindividual differences as well as context effects (e.g., time pressure, justification pressure, type of risk) may affect the relative weights of these goals. Dynamic me ntal repres entation of alternatives. RMDT assumes that initially a minimal mental representation of each alternative is constructed, including one or very few outcomes. This minimalist representation is elaborated during the 9

10 process, mainly for promising alternatives, by incorporating additional outcomes, events or RDOs. The uncertainty of outcomes and events usually is represented without probabilities. Elaboration and evaluative processes are closely intermingled (in contrast to, e.g., Prospect Theory). For example, only the representations of attractive incorporated for undes irable alternatives are expanded, and RDOs are outcomes. Dis cus s ion RMDT contradicts the central assumption of classical descriptive decision theory, namely that risky decisions generally can be modeled as gambles and that only the subjective values and probabilities of outcomes determine the decision. Otherwise, it has many components and aspects in common with other models. There is no room to go into details, but examples are heuristics like Maximin, and evaluative processes for values, uncertainty, etc. RMDT is compatible with many assumptions of other models, for example, different value functions for positive and negative outcomes in respect to a reference point (Kahneman & Tversky, 1979), or emotional influences (Loewenstein, Weber, Hsee & Welch, 2001). RMDM also can be integrated in general decision frameworks like, for example, Svenson s Differentiation and Consolidation theory (Svenson, 1992). The RDO part of the RMDT can be formulated as an independent process module that could be included into other decision models. Experimental research has revealed that gambles are a very specific type of risky tasks and that many results from experiments with gambles cannot be generalized to realistic scenarios. What distinguishes these types of risky tasks? Huber and Huber (2008) made a first attempt to unify gambles and non-gambling tasks. They identified experimentally two of the factors determining use of 10

11 probabilities and RDOs: the expectations to get useful probability information and to get useful RDO information. Table 1 presents a classification of decision tasks according to these two variables. Gambles are the prototypical example of a task with high probability expectation and low RDO expectation. The importance of RDOs in risky decisions has been established by numerous experiments of our research group and of others. Also, a number of factors have been discovered that affect the search for and the acceptance of RDOs. However, there are many open questions, such as: In which manner are the alternatives represented mentally, for example, as decision tree or as a mental causal model? Are there biases when people evaluate RDOs? For example, decision makers may overestimate the effect of an RDO with a very attractive alternative. Is the RMDT adaptable to situations where risk elimination is not the decision maker s goal (e.g., when a good chess player chooses an opponent)? Answers to these and other questions may further contribute to a future comprehensive theory of risky decision making. 11

12 Tab le 1 Examples of decision situations with high or low expectations to find useful RDO or probability information. expectation to find us eful RDO information high low high travel into country with a well-known infectious disease surgery (from point of view of patient) gamble s expectation to find us eful probability informatio n low economic investment in a developmental country with unclear economic and political situation (envisaged RDO: compensation guarantee from own government) stock market Note: Because expectations are subjective, different decision makers may categorize decision situations differently. 12

13 Ackno wledge me nts 1 Address correspondence to the author at: University of Fribourg, Department of Psychology, Route de Faucigny 2, CH-1701 Fribourg, Switzerland, oswald.huber@unifr.ch. I am very grateful to A.S. Bär and O.W. Huber (no relation) for many discussions. The introduction is based on our common papers. 13

14 References Bär, A. S., & Huber, O. (2008). Successful or unsuccessful search for risk defusing operators: Effect on decision behavior, European Journal of Cognitive Ps ychology, 20, Baron, J. (2008). Thinking and de ciding, Fourth edition. Cambridge, New York: Cambridge University Press. Huber, O. (2007). Behavior in risky decisions: Focus on risk defusing. In M. Abdellaoui, R.D. Luce, M. Machina, & B. Munier (Eds.): Uncertainty and Ris k (pp ). Berlin, New York: Springer. Huber, O., Bär, A. S., & Huber, O. W. (2009). Justification pressure in risky decision making: Search for risk defusing operators, Acta Psychologica, 130, Huber, O., & Huber, O. W. (2003). Detectability of the negative event: effect on the acceptance of pre- or post-event risk defusing actions, Acta Psychologica, 113, Huber, O., & Huber, O. W. (2008). Gambles vs. quasi-realistic scenarios: Expectations to find probability and risk-defusing information, Acta Ps ychologica, 127, Huber, O., & Macho, S. (2001). Probabilistic set-up and the search for probability information in quasi-naturalistic decision tasks, Ris k Decis ion and Policy, 6, Huber, O., Huber, O.W., & Bär. A.S. (2010). Information search and mental representation in risky decision making: the Advantages First Principle, Journal of Be havioral Decis ion Making. DOI: /bdm.674. Huber, O., Huber, O. W., & Schulte-Mecklenbeck, M. (2011). Determining the information participants need: Methods of Active Information Search. In M. 14

15 Schulte-Mecklenbeck, A. Kühberger, & R. Ranyard (Eds.), A handbook of proces s tracing methods for decision res earch (pp ). New York: Psychology Press. Huber, O., & Kunz, U. (2007). Time pressure in risky decision-making: effect on risk defusing, Psychology S cience, 49, Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk, Econometrica, 47, Loewenstein, G., Weber, E.U., Hsee, C., & Welch, N. (2001). Risk as feelings, Ps ychological Bulle tin, 127, Shiloh, S., Gerad, L., & Goldman B. (2006). Patients' information needs and decisionmaking processes: What can be learned from genetic counseling? Health Ps ychology, 25, Svenson, O. (1992). Differentiation and consolidation theory of human decision making: A frame of reference for the study of pre- and post-decision processes. Acta Ps ychologica, 80, Tyszka, T., & Zaleskiewicz, T. (2006). When does information about probability count in choices under risk? Risk Analys is, 26, Wilke, M., Haug, H., & Funke, J. (2008). Risk-specific search for risk-defusing operators, S wis s Journal of Psychology, 67, Williamson, J., Ranyard, R., & Cuthbert, L. (2000). Risk management in everyday insurance decisions: Evidence from a process tracing study, Ris k Decis ion and Policy, 5,

16 Recommended Readings Baron, J. (2008). See reference list. A comprehensive and clearly written introduction into decision theory. Huber, O. (2007). See reference list. A comprehensive review of research on risk defusing up to Huber, O., Wider, R. and Huber, O.W. (1997). Active information search and complete information presentation in naturalistic risky decision tasks. Acta Ps ychologica, 95, This is the first paper raising the topic of risk defusing operators. Payne, J.W., Bettman, J.R., & Johnson, E.J. (1993). The Adaptative Decis ion Make r. Cambridge University Press, New York. A classical text on heuristics in the decision process. Shah, A.K., & Oppenheimer, D.M. (2008). Heuristics made easy: An effort-reduction framework, Psychological Bulle tin, 134, An up-to-date thorough overview and analysis of decision and judgmental heuristics. 16

17 Figure 2: Ris k Manage ment Decis ion Theory 17