Decision Making

Decision  making  (DM)  is  the  cognitive  operation of selecting a response from a range of available  responses  in  circumstances  where  an  action is needed. DM usually takes place while interacting with either the external environment or internal  desires  and  requirements.  Decisions  may  be made  by  an  individual  or  a  group,  which  mediates between the environment and the behavior or performance.  DM  can  operate  slowly  under  conditions  that  lack  environmental  constraints  and demands but must be fast under circumstances of pressure, stress, or temporal constraints. When circumstances  permit,  the  brain  processes  the  information  needed  for  DM  intentionally  through  a perceptual–cognitive linkage.

The  environmental  information  needed  for  a decision  to  be  made  is  perceived  by  the  senses, mainly the visual system in sport-related environment.  The  visual  system  enables  capture  of  environmental  stimuli  and  perceives  patterns  in  the visual  field,  and  feed-forward  of  this  information to  long-term  memory  (LTM)  allows  anticipatory decisions to be made or a response decision to be primed.  Such  a  process  is  slow,  deliberate,  and intentional.  It  operates  under  conscious  control and  can  be  modified  and  altered  if  time  allows. In  contrast,  an  emergent-type  of  DM  is  spontaneous,  automatic,  self-organized,  and  relies  on established  perception–action  couplings.  Such a  DM  triggers  responses  that  are  either  retrievable  automatically  from  LTM  (in  experts  who have  accumulated  many  hours  of  practice  and experience)  or  used  randomly  with  the  possibility  of  a  high  rate  of  error  (in  novices  who  have acquired limited hours of practice and situational exposure).  Repeated  exposure  to  similar  situations,  stimuli,  tasks,  and  environments  may  turn DM  operations  from  a  deliberate–slow  process into  an  automatic–fast  process.  Experience  and expertise  allow  DM  to  shift  from  an  intentional and  deliberate  mode  into  an  automatic  mode  of operation when the environmental conditions and constraints  necessitate  this  shift.  The  ability  to shift  between  different  attention  and  DM  modes enables the cognitive system to operate efficiently by increasing the probability of making the right decision at the right time and avoiding errors, detrimental to performance.

Approaches to Decision Making

DM  has  been  given  much  attention  in  the  military,  business,  economic,  gambling,  and  statistics domains. While the approaches related to the types of  DM  in  each  of  these  domains  differ,  each  of these approaches has some relevance to sport. We describe  each  of  these  approaches  briefly  before examining  the  DM  concept  in  the  sport  domain. The  prescriptive  approach  to  DM  (the  prescriptive theoretical normative model) views the person as  a  goal or  outcome-oriented  creature  attempting to maximize effort toward goal attainment. In gambling, where uncertainty is an inherent condition,  probabilistic  estimates  are  used  to  arrive  at an optimal solution (e.g., the winning DM).

The  cognitive-oriented  approach  to  DM  relies on  the  supposition  that  the  person’s  cognitive capabilities  are  limited.  However,  repeated  exposure  and  domain-specific  experience  circumvents these  limitations  by  allowing  the  decision  maker to  efficiently  capture  visual  and  logical  patterns, deliver  them  to  LTM  via  the  operation  of  long-term working memory (LTWM), and accordingly retrieve responses that are stored in a rich network of mental representations.

The  naturalistic-descriptive  approach  to  DM (NDA) consists of both rational and irrational processes,  and  incorporates  personal  values,  morals, motivation, personal state, and emotions; all affect the  personal  DM  process.  NDM  models,  such  as the  image  theory,  explanation-based  theory,  recognition-primed  DM  (RPD),  and  cue-retrieval  of action attempted to account for the DM behaviors. Heuristics (e.g., a method of solving a problem for which no formula exists, based on informal methods  or  experience,  and  employing  a  form  of  trial and  error  iteration)  have  been  offered  within  the NDA  to  account  for  the  underlying  mechanisms of  DM  in  the  real  world.  The  RPD  postulates that DM consists of cue identification, situational goals, alternative action generations, and expectations  for  possible  alterations;  and  all  are  affected by experience. The more complex the situation is, the more practice is needed for adjustment to take place. This concept is based on mental representations  (knowledge  structure)  for  guiding,  monitoring, and executing the decision process. The NDA is  therefore  a  knowledge-driven  discourse,  which consists of accumulating both the declarative and procedural  neural  circuits  necessary  for  DM  in situations  that  vary  in  complexity  and  certainty. The  RPD  within  the  NDA  is  an  approach  that influenced the current concepts of DM in the sport and exercise domain.

Decision Making in Sport

The approach to DM employed in almost all sport research has been heavily influenced by the RPD naturalistic  concept,  but  modified  for  the  unique environment  of  each  sport.  For  example,  DM  in rifle  shooting  pertains  to  attending  to  internal bodily  cues  and  pulling  the  trigger  at  the  right time. DM in dynamic and fast sports, such as soccer, football, hockey, basketball, handball, volleyball, water polo, and racquetball, is dependent on visual–spatial  attention  strategy  (mainly  visual), cue-priming,  attention  flexibility,  selectivity,  pattern  recognition,  anticipatory  mechanisms,  and the  ability  to  assign  probabilities  to  sequential  events  (to  prime  responses),  all  of  which  are governed  by  mental  representations,  the  neural schemas  containing  declarative  and  procedural knowledge. Extensive exposure to the sport environment  may  result  in  the  intentional  conscious control of information processing and DM being replaced, at least in part, by more automatic control  processes  that  allow  the  attention  system  to be more flexible and seek information from more than one source in parallel. Thus, the efficiency of the information processing system in making decisions is dependent on the richness and structure of the knowledge system (the mental representations network).  The  ability  to  encode  information  via the perceptual system, deliver it to the higher level processing  system  via  LTWM,  and  process  and retrieve responses are all a function of the extent to which the knowledge system is well developed and structured.

When  an  athlete  chokes  under  pressure,  a breakdown  in  the  mental  representation  network occurs,  and  the  perceptual–cognitive–motor  linkage  becomes  dysfunctional.  Specifically,  under conditions of emotional or temporal pressure, the perceptual–cognitive  system  ceases  to  function appropriately and the probability of an erroneous decision being made increases substantially. Thus, coping  with  stress  must  be  taken  into  account within the DM conceptualization.

Competitive sport events are laden with social and  emotional  stressors.  Information  processing under  pressure  may  be  affected  in  that  attention   is   narrowed,   which   inhibits   recognition and  selection  of  essential  environment  cues.  In turn,  the  cognitive  system  has  limited  resources to  establish  visual  and  meaningful  patterns  and prime  a  response.  Instead,  the  cognitive  system becomes  overloaded  with  interfering  thoughts, and  attempts  to  control  emotions  are  accompanied by declined self-efficacy. Under such stressful conditions,  DM  is  expected  to  suffer  because  of the inability to prime and trigger the appropriate response,  and  this,  in  turn,  is  likely  to  result  in performance  decline.  Existing  evidence  supports the notion that the quality of DM depends on how pressure  is  appraised  and  interpreted  and  what coping  strategies  and  self-regulation  are  applied by the athlete in an effort to maintain the operating efficiency of the perceptual–cognitive system.

References:

  1. Tenenbaum, G. (2003). Expert athletes: An integrated approach to decision making. In J. L. Starkes &
  2. A. Ericsson (Eds.), Expert performance in sports: Advances in research on sport expertise (pp. 192–218). Champaign, IL: Human Kinetics. Tenenbaum, G. (2004). Decision making in sport. In Spielberger (Ed.), Encyclopedia of applied psychology (Vol. 1, pp. 575–584). San Diego, CA: Elsevier.
  3. Tenenbaum, G., & Bar-Eli, M. (1993). Decision making in sport. In R. N. Singer, M. Murphey, & L. K. Tennant (Eds.), Handbook on research in sport psychology (pp. 171–192). New York: Macmillan.
  4. Zsambok, C. E., & Klein, G. (Eds.). (1997). Naturalistic decision making. Mahwah, NJ: Erlbaum.

See also:

Decision Making

Decision Making Definition

Decision making refers to the act of evaluating (i.e., forming opinions of) several alternatives and choosing the one most likely to achieve one or more goals. Common examples include deciding for whom to vote, what to eat or buy, and which college to attend. Decision making plays a key role in many professions, such as public policy, medicine, and management. The related concept of judgment refers to the use of information, often from a variety of sources, to form an evaluation or expectation. One might imagine that people’s judgment determines their choices, though it is not always the case.

Decision Making Background

Decision MakingTheories of decision making were originally developed by philosophers, mathematicians, and economists, who focused on how people make choices to achieve often conflicting goals. Following the work of early theorists such as John von Neumann and Oskar Morgenstern and Leonard Savage, a theory called subjective expected utility theory has become particularly influential. This theory distinguishes between the decision maker’s values (otherwise called his or her utilities) and expectations or beliefs. The key assumption is that people select the option that is associated with the highest overall expected utility. In plain terms, you pick the best option, and so decision making is about figuring out what is the best choice.

Expected utility theory and decision theory have focused on normative aspects (i.e., what people should do), whereas behavioral decision theory and the general field of behavioral decision making have focused on descriptive aspects of decision making (i.e., what people actually do to form judgments and make choices). It is noteworthy that, although expected utility theory was derived from economic principles of rational behavior rather than based on studies of human behavior, economists and researchers in many other fields have assumed that the theory also describes actual behavior and that departures from rational choice would eventually correct themselves based on learning and external forces.

This assumption, in turn, led to a great deal of behavioral decision research, which has documented a wide range of violations of utility maximization, that is, cases in which people pick something other than what is objectively the best option. Thus, research findings have often been seen as interesting to the extent that they appeared surprising and inconsistent with expected utility theory. Such research has shown that expected utility theory is often inadequate. Furthermore, the theory does not address many of the key aspects of judgment and decision making, such as the selection of information and options to be considered, the manner in which a decision maker might trade off the considered attributes of the options, and the impact of affective and social factors. Moreover, expected utility theory does not address the process of judgment and decision making.

A cognitive scientist named Herbert Simon introduced the concept of bounded rationality, which is an idea that takes into account the fact that people only have a limited cognitive ability to process information. Because of limited processing ability, instead of maximizing utility (i.e., picking the objectively best option), people may satisfice; that is, they may choose an option that is good enough, even though it may often not be the overall best. Limited cognitive capacity also implies that people will tend to rely on shortcuts or simplifying strategies, referred to as heuristics, which typically produce satisfactory decisions, though in some cases they may produce errors.

Despite the initial emphasis on demonstrating violations of rationality and expected utility theory, behavioral decision theory research has become more psychological and process oriented. Thus, following research in social and cognitive psychology, researchers have started employing various process measures (e.g., verbal protocols) and manipulations that were designed to provide a better understanding of the processes underlying judgment and choice.

How Judgments and Decisions Are Made

Behavioral research on judgment and decision making has documented numerous violations of normative models that were previously relied upon. The following discussion briefly reviews a few important examples.

Judgment Heuristics and Biases

The theory of rational choice has assumed that people are generally capable of computing and making unbiased judgments. However, a great deal of research has demonstrated that people’s assessments of probabilities and values are often inconsistent with basic laws of probability. Going beyond the notion of bounded rationality, psychologists Amos Tversky and Daniel Kahneman advanced three heuristics that play a key role in intuitive judgments of probabilities, magnitudes, and frequencies: representativeness, availability, and anchoring. According to the representativeness heuristic, people judge the likelihood that X is a Y based on their assessment of the degree to which X resembles Y. For example, when assessing the likelihood that a student specializes in poetry, people assess the similarity between that student and the prototypical poet.

The availability heuristic indicates that people assess the frequency and probability of an event or a characteristic based on the ease with which examples come to mind. For example, in one demonstration, a group of respondents estimated the number of seven-letter words (in a few book pages) that end with ing, whereas a second group estimated the number of seven-letter words with n in the sixth position. Consistent with the availability heuristic, the former estimate was much higher than the latter (even though any seven-letter word that ends with ing necessarily has n in the sixth position).

Anchoring refers to a process of assessing values whereby people who start from an anchor tend to end up with a value that is close to the initial anchor. For example, people estimated that Gandhi lived until the age of 67 after being asked if he died before or after the age of 140, whereas those asked if he had died before or after the age of 9 estimated that he had died at the age of 50. Similar anchoring effects have been observed even when the anchor was clearly arbitrary, such as when people make an estimate by deciding whether the true value is above or below the last two digits of their own social security number.

Prospect Theory

Kahneman and Tversky’s prospect theory represents an influential, comprehensive attempt to revise and address key violations of the standard expected utility model. That is, those two researchers tried to formulate a general explanation of the reasons people fail to make the best choice. Options are evaluated as gains or losses relative to a reference point, which is to say that it is not the absolute effect that matters but whether the event has positive or negative implications for one’s current standing. This has often been applied to money: The data show that it’s not the same to gain $10,000 for a poor person as it is for a rich person, because the gain is much greater for the person whose current wealth is very little.

In general, most people tend to be risk averse for gains and risk seeking for losses. Risk aversion can be thought of like this: A person facing two options, one of which is a surer bet but has a smaller payout compared to the other, which is more uncertain to be obtained but with a larger payout, would be predicted to choose the option that will bring a surer but smaller payout. Risk seeking (or risk tolerance it is also called) is the opposite. Imagine a person facing a choice between two options, one of which is more certain to happen. Prospect theory and many experiments that have tested it have shown that people prefer the larger (riskier) loss that has less certainty to happen.

Another important point from prospect theory is loss aversion—losses have a greater impact psychologically than similar gains. In other words, losing $500 hurts a lot more, psychologically, than finding $500 brings pleasure. The property of loss aversion is related to endowment effect and the status quo bias.

The Construction of Preferences

A great deal of decision-making research since around 1975 has led to a growing consensus that preferences for options are often constructed when decisions need to be made, rather than when they are retrieved from a master list of preferences stored in memory. This means that people tend to make decisions because of “on-the-spot” feelings or ideas rather than some deep, ingrained beliefs that they constantly use to make choices. This means that choices are sensitive to the framing of the options, the choice context, and the preference elicitation task.

With respect to framing, it has been shown, for example, that (a) framing options as losses rather than as gains leads to more risk-seeking preferences, and (b) framing (cooked) ground beef in terms of how lean it is (e.g., 80% lean) rather than how much fat it contains (20% fat, even though that conveys the same message about the meat as 80% lean) produces more positive evaluations of the beef’s taste. Regarding the impact of the choice context (or choice set configuration), it has been shown that adding an asymmetrically dominated option (e.g., adding an unattractive pen to a choice set consisting of an attractive pen and $6 in cash) increases the share of the dominating option (the attractive pen).

It has also been shown that an option often is chosen more often, relative to how often other options are chosen, when there is a “compromise” (a middle) option in the set. With respect to the preference elicitation task, studies have shown, for example, that performing what is called a matching task (i.e., the person is asked to enter a value that makes two options equally attractive) leads to different preferences than when people simply perform a choice task—despite the fact that the options that are presented are the same, and the only difference is the method used by the person to evaluate the options. Similarly, ratings or evaluations of individual options tend to produce systematically different preferences than choices or other tasks involving joint evaluation of options.

Current Directions in Decision Research

As the question of whether expected utility model adequately describes decision making has been largely resolved, decision researchers have tried to gain a better understanding of how decisions are actually made, often using various process measures and task manipulations. Furthermore, researchers have examined a wider range of judgment and decision-making dimensions and have addressed topics that were previously regarded as the domain of other fields, such as social and cognitive psychology and business administration.

Process Measures

Whereas earlier decision research was focused on the outcomes of decisions, it has become clear that decision processes can provide important insights into decision making, because they are influenced by task and option variations that may often not influence decision outcomes. It was initially assumed that decision makers apply particular decision rules, such as forming an evaluation of an option by adding the positive aspects of that option and subtracting the negative aspects (e.g., weighted additive [compensatory] model), or by choosing important aspects of the decision and then choosing based on whether options do or do not reach a certain cutoff in that domain (e.g., conjunctive rule, or lexicographic decision rules). However, consistent with the notion of constructed preferences, subsequent research has shown that decision makers typically combine fragments of decision rules, such as starting by eliminating options that do not meet certain standards and then using the adding positives/subtracting negatives compensatory rules to evaluate the remaining options.

Early process-oriented decision research relied largely on process measures, such as response latencies, the percentage of intradimensional versus inter-dimensional comparisons, and verbal protocols. Such measures can provide rich data, though concerns might arise whether the behavior and responses that are captured accurately represent naturally occurring decision processes. A complementary research approach, similar to many studies in psychology, is to rely on task conditions (e.g., cognitive load, time pressure), stimulus manipulations, and individual differences from which one could infer the underlying decision processes and moderators of the observed decision outcomes.

The Role of Affect in Decision Making

Most decision research has focused on what might be seen as objective evaluation of options based on attributes such as the probability of winning and the payoff. However, there is a growing recognition that decisions are often influenced by the affective reactions to options. Affect refers to the emotional reaction to the “goodness” (or attractiveness) of options, which is often triggered automatically without much (or any) thought. It has been suggested that such automatic, affective reactions are often the main drivers of judgments and decisions, with conscious, deliberate arguments merely serving to explain those decisions. Researchers have used a wide range of methodologies to examine the role, primacy, and speed of affective reactions to decision stimuli, such as subliminal priming, the observation of patients whose affective processing ability was damaged, and the impact of putting respondents in a positive or negative mood.

The Two-System View of Judgment and Decision Making

Evaluations based on automatic, affective reactions belong to a broader class of judgments and decisions that tend to be done intuitively and automatically, without any deliberate evaluation. It is now believed that such processes may characterize many, perhaps most, judgments and decisions, whereas more deliberate, slow, reason-based processes are activated as needed, sometimes correcting or overriding the automatically produced responses. Although intuitive, automatic responses have been shown to influence both judgments and choices, deliberate evaluations of options and their attributes tend to play a greater role in choice. Indeed, viewing choice as driven by the balance of reasons for and against options has been shown to account for choice anomalies (e.g., the asymmetric dominance and compromise effects discussed earlier), which are more difficult to explain based on value maximization or based solely on the notion that decisions are made automatically, with little consideration of attributes or the relations among options.

Social and Cultural Aspects of Decision Making

In addition to considering the implications of task and stimuli characteristics for decision processes and outcomes, decision researchers have studied the role of social and cultural factors and individual differences in decision making. Some social aspects, such as conformity, have received relatively little emphasis, despite their clearly important role in decision making, in part because they appear straightforward and not surprising. However, researchers have examined, for example, the ability of social conditions, such as accountability and having to justify to others, to moderate and possibly diminish people’s susceptibility to various judgment and decision errors. By and large, similar to other types of incentives such as giving monetary compensation for good performance, research has shown that social incentives have limited beneficial impact on decision performance, though they could diminish some errors that are due to limited effort. There also has been a growing interest in the role of cross-cultural differences in decision performance. Initially, researchers focused on the differences between “individualistic” (e.g., people in the United States and Western Europe) and “collectivist” (e.g., Asian) societies, for example, showing that Chinese tend to be more susceptible than Westerners to the overconfidence bias. More recent research suggests that cross-cultural differences in judgment and decision making are less robust than previously thought and are sensitive to various situational factors.

Decision Making Research in Applied Fields

Most behavioral decision researchers now reside in business schools rather than in psychology departments. This shift reflects, in part, the growing influence of decision research on applied fields, such as marketing, organizational behavior, and behavioral economics. For example, a great deal of behavioral decision research over the past 30 years or so has examined topics related to consumer decision making, bargaining, fairness, and behavioral game theory. Furthermore, there is a growing recognition in the economics field, which dominated early views of decision making, that violations of rationality are often systematic, predictable, and are not corrected by learning or market forces. Accordingly, the still evolving subfield of behavioral economics has increasingly incorporated descriptive aspects of decision making, derived from studies conducted by behavioral decision researchers, into economic models, addressing issues such as choice, valuation of goods, and discrimination.

References:

  1. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of choice under risk. Econometrica, 47(2), 263-291.
  2. Savage, L. J. (1954). Foundations of statistics. Oxford, UK: Wiley.
  3. Simon, H. A. (1957). Models of man: Social and rational. Oxford, UK: Wiley.
  4. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4151), 1124-1131.
  5. Von Neumann, J., & Morgenstern, O. (1941). Theory of games and economic behavior (2nd ed.). Princeton, NJ: Princeton University Press.