Joseph G. Johnson

Take The First: Option-generation and resulting choices☆

Abstract Experimental decision-making research often uses a task in which participants are presented with alternatives from which they must choose. Although tasks of this type may be useful in determining measures (e.g., preference) related to explicitly stated alternatives, they neglect an important aspect of many real-world decision-making environments—namely, the option-generation process. The goal of the present research is to extend previous literature that fills this void by presenting a model that attempts to describe the link between the use of different strategies and the subsequent option-generation process, as well as the resulting choice characteristics. Specifically, we examine the relationship between strategy use, number and order of generated options, choice quality, and dynamic inconsistency. “Take The First” is presented as a heuristic that operates in ill-defined tasks, based on our model assumptions. An experiment involving a realistic (sports) situation was conducted on suitable participants (athletes) to test the predictions of the model. Initial results support the model’s key predictions: strategies producing fewer generated options result in better and more consistent decisions.

Domain Specificity in Experimental Measures and Participant Recruitment An Application to Risk-Taking Behavior

We challenge the prevailing notion that risk taking is a stable trait, such that individuals show consistent risk-taking/aversive behavior across domains. We subscribe to an alternative approach that appreciates the domain-specific nature of risk taking. More important, we recognize heterogeneity of risk profiles among experimental samples and introduce a new methodology that takes this heterogeneity into account. Rather than using a convenient subject pool (i.e., university students), as is typically done, we specifically targeted relevant subsamples to provide further validation of the domain-specific nature of risk taking. Our research shows that individuals who exhibit high levels of risk-taking behavior in one content area (e.g., bungee jumpers taking recreational risks) can exhibit moderate levels in other risky domains (e.g., financial). Furthermore, our results indicate that risk taking among targeted subsamples can be explained within a cost-benefit framework and is largely mediated by the perceived benefit of the activity, and to a lesser extent by the perceived risk.

Expertise-based differences in search and option-generation strategies.

The current work builds on option-generation research using experts of various skill levels in a realistic task. We extend previous findings that relate an athletes performance strategy to generated options and subsequent choices in handball. In a 2-year longitudinal study, we present eye-tracking data to independently verify decision strategies previously inferred from patterns of generated options. A verbal protocol identified the option-generation process for each individual prior to an allocation decision. Although athletes of varying expertise generated the same number of options on average, these options differed in quality between expert, near-expert, and nonexpert athletes for both their initial and final choices. These and other key results are formalized to elaborate a model of option generation, deliberation, and selection.

A Dynamic, Stochastic, Computational Model of Preference Reversal Phenomena

Preference orderings among a set of options may depend on the elicitation method (e.g., choice or pricing); these preference reversals challenge traditional decision theories. Previous attempts to explain these reversals have relied on allowing utility of the options to change across elicitation methods by changing the decision weights, the attribute values, or the combination of this information--still, no theory has successfully accounted for all the phenomena. In this article, the authors present a new computational model that accounts for the empirical trends without changing decision weights, values, or combination rules. Rather, the current model specifies a dynamic evaluation and response process that correctly predicts preference orderings across 6 elicitation methods, retains stable evaluations across methods, and makes novel predictions regarding response distributions and response times.

Building bridges between neural models and complex decision making behaviour

Diffusion processes, and their discrete time counterparts, random walk models, have demonstrated an ability to account for a wide range of findings from behavioural decision making for which the purely algebraic and deterministic models often used in economics and psychology cannot account. Recent studies that record neural activations in non-human primates during perceptual decision making tasks have revealed that neural firing rates closely mimic the accumulation of preference theorized by behaviourally-derived diffusion models of decision making. This article bridges the expanse between the neurophysiological and behavioural decision making literatures specifically, decision field theory [Busemeyer, J. R. & Townsend, J. T. (1993). Decision field theory: A dynamic-cognitive approach to decision making in an uncertain environment. Psychological Review, 100, 432-459], a dynamic and stochastic random walk theory of decision making, is presented as a model positioned between lower-level neural activation patterns and more complex notions of decision making found in psychology and economics. Potential neural correlates of this model are proposed, and relevant competing models are also addressed.

A Tri-Reference Point Theory of Decision Making Under Risk

The tri-reference point (TRP) theory takes into account minimum requirements (MR), the status quo (SQ), and goals (G) in decision making under risk. The 3 reference points demarcate risky outcomes and risk perception into 4 functional regions: success (expected value of x ≥ G), gain (SQ < × < G), loss (MR ≤ x < SQ), and failure (x < MR). The psychological impact of achieving or failing to achieve these reference points is rank ordered as MR > G > SQ. We present TRP assumptions and value functions and a mathematical formalization of the theory. We conducted empirical tests of crucial TRP predictions using both explicit and implicit reference points. We show that decision makers consider both G and MR and give greater weight to MR than G, indicating failure aversion (i.e., the disutility of a failure is greater than the utility of a success in the same task) in addition to loss aversion (i.e., the disutility of a loss is greater than the utility of the same amount of gain). Captured by a double-S shaped value function with 3 inflection points, risk preferences switched between risk seeking and risk aversion when the distribution of a gamble straddled a different reference point. The existence of MR (not G) significantly shifted choice preference toward risk aversion even when the outcome distribution of a gamble was well above the MR. Single reference point based models such as prospect theory cannot consistently account for these findings. The TRP theory provides simple guidelines for evaluating risky choices for individuals and organizational management.

Decision making under risk and uncertainty.

Decision making is studied from a number of different theoretical approaches. Normative theories focus on how to make the best decisions by deriving algebraic representations of preference from idealized behavioral axioms. Descriptive theories adopt this algebraic representation, but incorporate known limitations of human behavior. Computational approaches start from a different set of assumptions altogether, focusing instead on the underlying cognitive and emotional processes that result in the selection of one option over the other. This review comprehensively but concisely describes and contrasts three approaches in terms of their theoretical assumptions and their ability to account for behavioral and neurophysiological evidence from experimental research. Although each approach contributes substantially to our understanding of human decision making, we argue that the computational approach is more fruitful and parsimonious for describing and predicting choices in both laboratory and applied settings and for understanding the neurophysiological substrates of decision making. Copyright

Individual Differences of Action Orientation for Risk Taking in Sports

Abstract The goal of this article is to explain empirical risk-taking behavior in sports from an individual cognitive modeling perspective. A basketball task was used in which participants viewed four video options that varied in the degree of associated risk. The participants were independently classified by scores on the Questionnaire for Assessing Prospective Action Orientation and State Orientation in Success, Failure, and Planning Situations as action-oriented or state-oriented decision makers. The results of the experiment show that action-oriented players shoot faster and more often to the basket and that state-oriented players prefer to pass to a playmaker more often. Four versions of a computational model of decision making, Decision Field Theory, were compared to evaluate whether behavioral differences depend on the focus of attention, the initial preferences, threshold values, or an approach-avoidance interpretation of the task. Different starting preferences explained individual choices and decision times most accurately. Risk taking in basketball shooting behavior can be best explained by different preferences for starting values for risky and safe options caused by different levels of action orientation.

Decision moving window: using interactive eye tracking to examine decision processes

It has become increasingly more important for researchers to better capture the complexities of making a decision. To better measure cognitive processes such as attention during decision making, we introduce a new methodology: the decision moving window, which capitalizes on both mouse-tracing and eye-tracking methods. We demonstrate the effectiveness of this methodology in a probabilistic inferential decision task where we reliably measure attentional processing during decision making while allowing the person to determine how information is acquired. We outline the advantages of this methodological paradigm and how it can advance both decision-making research and the development of new metrics to capture cognitive processes in complex tasks.

The Cambridge Handbook of Computational Psychology: Micro-Process Models of Decision Making

Computational models are like the new kids in town for the field of decision making. This field is largely dominated by axiomatic utility theories (Bell, Raiffa, & Tversky, 1998; Luce, 2000) or simple heuristic rule models (Gigerenzer, Todd, & the ABC Research Group, 1999; Payne, Bettman, & Johnson, 1993). It is difficult for “the new kids” to break into this field for a very important reason: They just seem too complex in comparison. Computational models are constructed from a large number of elementary units that are tightly interconnected to form a complex dynamical system. So the question, “what does this extra complexity buy us?,” is raised. Computational theorists first have to prove that their models are worth the extra complexity. This chapter provides some answers to that challenge. First, the current state of decision research applied to preferences under uncertainty is reviewed. The evolution of the algebraic utility approach that has dominated the field of decision making is described, showing a steady progression away from a simple and intuitive principle of maximizing expected value. The development of utility theories into their current form has included modifications for the subjective assessment of objective value and probability, with the most recent work focusing on finer specification of the latter. The impetus for these modifications is then discussed; in particular, specific and pervasive “paradoxes” of human choice behavior are briefly reviewed. This section arrives at the conclusion that no single utility theory provides an accurate descriptive model of human choice behavior. Then, computational approaches to decision making are introduced, which seem more promising in their ability to capture robust trends in human choice behavior. This advantage is due to their common focus on the micro-mechanisms of the underlying deliberation process, rather than solely on the overt choice behavior driven by choice stimuli. A number of different approaches are introduced, providing a broad survey of the current corpus of computational models of decision making. The fourth section focuses on one particular model to offer a