Reference-Point Theory: An Account of Individual Differences in Risk Preferences.

We propose an account of individual differences in risk preferences called "reference-point theory" for choices between sure things and gambles. Like most descriptive theories of risky choice, preferences depend on two drivers-hedonic sensitivities to change and beliefs about risk. But unlike most theories, these drivers are estimated from judged feelings about choice options and gamble outcomes. Furthermore, the reference point is assumed to be the less risky option (i.e., sure thing). Loss aversion (greater impact of negative change than positive change) and pessimism (belief the worst outcome is likelier) predict risk aversion. Gain seeking (greater impact of positive change than negative change and optimism (belief the best outcome is likelier) predict risk seeking. But other combinations of hedonic sensitivities and beliefs are possible, and they also predict risk preferences. Finally, feelings about the reference point predict hedonic sensitivities. When decision makers feel good about the reference point, they are frequently loss averse. When they feel bad about it, they are often gain seeking. Three studies show that feelings about reference points, feelings about options and feelings about outcomes predict risky choice and help explain why individuals differ in their risk preferences.

Human and Algorithmic Predictions in Geopolitical Forecasting: Quantifying Uncertainty in Hard-to-Quantify Domains.

Research on clinical versus statistical prediction has demonstrated that algorithms make more accurate predictions than humans in many domains. Geopolitical forecasting is an algorithm-unfriendly domain, with hard-to-quantify data and elusive reference classes that make predictive model-building difficult. Furthermore, the stakes can be high, with missed forecasts leading to mass-casualty consequences. For these reasons, geopolitical forecasting is typically done by humans, even though algorithms play important roles. They are essential as aggregators of crowd wisdom, as frameworks to partition human forecasting variance, and as inputs to hybrid forecasting models. Algorithms are extremely important in this domain. We doubt that humans will relinquish control to algorithms anytime soon-nor do we think they should. However, the accuracy of forecasts will greatly improve if humans are aided by algorithms.

Conflicting Goals Influence Physicians' Expressed Beliefs to Patients and Colleagues.

BACKGROUND: Physicians who communicate their prognostic beliefs to patients must balance candor against other competing goals, such as preserving hope, acknowledging the uncertainty of medicine, or motivating patients to follow their treatment regimes. OBJECTIVE: To explore possible differences between the beliefs physicians report as their own and those they express to patients and colleagues. DESIGN: An online panel of 398 specialists in internal medicine who completed their medical degrees and practiced in the United States provided their estimated diagnostic accuracy and prognostic assessments for a randomly assigned case. In addition, they reported the diagnostic and prognostic assessments they would report to patients and colleagues more generally. Physicians answered questions about how and why their own beliefs differed from their expressed beliefs to patients and colleagues in the specific case and more generally in their practice. RESULTS: When discussing beliefs about prognoses to patients and colleagues, most physicians expressed beliefs that differed from their own beliefs. Physicians were more likely to express greater optimism when talking to patients about poor prognoses than good prognoses. Physicians were also more likely to express greater uncertainty to patients when prognoses were poor than when they were good. The most common reasons for the differences between physicians' own beliefs and their expressed beliefs were preserving hope and acknowledging the inherent uncertainty of medicine. CONCLUSION: To balance candor against other communicative goals, physicians tended to express beliefs that were more optimistic and contained greater uncertainty than the beliefs they said were their own, especially in discussions with patients whose prognoses were poor.

From discipline-centered rivalries to solution-centered science: Producing better probability estimates for policy makers.

From 2011 to 2015, the U.S. intelligence community sponsored a series of forecasting tournaments that challenged university-based researchers to invent measurably better methods of forecasting political events. Our group, the Good Judgment Project, won these tournaments by balancing the collaboration and competition of members across disciplines. At the outset, psychologists were ahead of economists in identifying individual differences in forecasting skill and developing methods of debiasing forecasts, whereas economists were ahead of psychologists in designing simple market mechanisms that distilled predictive signals from noisy individual-level data. Working closely with statisticians, psychologists eventually beat the markets by producing better probability estimates that funneled top forecasters into elite teams and aggregated their judgments using a log-odds formula tuned to the diversity of the forecasters. Our research group performed best when team members strove to get as much as possible from their home disciplines, but acknowledged their limitations and welcomed help from outsiders. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Bringing probability judgments into policy debates via forecasting tournaments.

Political debates often suffer from vague-verbiage predictions that make it difficult to assess accuracy and improve policy. A tournament sponsored by the U.S. intelligence community revealed ways in which forecasters can better use probability estimates to make predictions-even for seemingly "unique" events-and showed that tournaments are a useful tool for generating knowledge. Drawing on the literature about the effects of accountability, the authors suggest that tournaments may hold even greater potential as tools for depolarizing political debates and resolving policy disputes.

Anticipated regret in shared decision-making: a randomized experimental study.

BACKGROUND: Explicit consideration of anticipated regret is not part of the standard shared decision-making protocols. This pilot study aimed to compare decisions about a hypothetical surgery for breast cancer and examined whether regret is a consideration in treatment decisions. METHODS: In this randomized experimental study, 184 healthy female volunteers were randomized to receive a standard decision aid (control) or one with information on post-surgical regret (experimental). The main outcome measures were the proportion of subjects choosing lumpectomy vs. mastectomy and the proportion reporting that regret played a role in the decision made. We hypothesized that a greater proportion of the experimental group (regret-incorporated decision aid) would make a surgical treatment preference that favored the less regret-inducing option and that they would be more likely to consider regret in their decision-making process as compared to the control group. RESULTS: A significantly greater proportion of the experimental group subjects reported regret played a role in their decision-making process compared to the control counterparts (78 vs. 65 %; p = 0.039). Recipients of the regret-incorporated experimental decision aid had a threefold increased odds of choosing the less regret-inducing surgery (OR = 2.97; 95 % CI = 1.25, 7.09; p value = 0.014). CONCLUSIONS: In this hypothetical context, the incorporation of regret in a decision aid for preference-sensitive surgery impacted decision-making. This finding suggests that keying in on anticipated regret may be an important element of shared decision-making strategies. Our results make a strong argument for applying this design and pursuing further research in a surgical patient population. TRIAL REGISTRATION: Clinicaltrials.gov, NCT02563808.

Balancing Fairness and Efficiency: The Impact of Identity-Blind and Identity-Conscious Accountability on Applicant Screening.

This study compared two forms of accountability that can be used to promote diversity and fairness in personnel selections: identity-conscious accountability (holding decision makers accountable for which groups are selected) versus identity-blind accountability (holding decision makers accountable for making fair selections). In a simulated application screening process, undergraduate participants (majority female) sorted applicants under conditions of identity-conscious accountability, identity-blind accountability, or no accountability for an applicant pool in which white males either did or did not have a human capital advantage. Under identity-conscious accountability, participants exhibited pro-female and pro-minority bias, particularly in the white-male-advantage applicant pool. Under identity-blind accountability, participants exhibited no biases and candidate qualifications dominated interview recommendations. Participants exhibited greater resentment toward management under identity-conscious accountability.

Improving Intelligence Analysis With Decision Science.

Intelligence analysis plays a vital role in policy decision making. Key functions of intelligence analysis include accurately forecasting significant events, appropriately characterizing the uncertainties inherent in such forecasts, and effectively communicating those probabilistic forecasts to stakeholders. We review decision research on probabilistic forecasting and uncertainty communication, drawing attention to findings that could be used to reform intelligence processes and contribute to more effective intelligence oversight. We recommend that the intelligence community (IC) regularly and quantitatively monitor its forecasting accuracy to better understand how well it is achieving its functions. We also recommend that the IC use decision science to improve these functions (namely, forecasting and communication of intelligence estimates made under conditions of uncertainty). In the case of forecasting, decision research offers suggestions for improvement that involve interventions on data (e.g., transforming forecasts to debias them) and behavior (e.g., via selection, training, and effective team structuring). In the case of uncertainty communication, the literature suggests that current intelligence procedures, which emphasize the use of verbal probabilities, are ineffective. The IC should, therefore, leverage research that points to ways in which verbal probability use may be improved as well as exploring the use of numerical probabilities wherever feasible.

What might have been? The role of the ventromedial prefrontal cortex and lateral orbitofrontal cortex in counterfactual emotions and choice.

Counterfactual feelings of regret occur when people make comparisons between an actual outcome and a better outcome that would have occurred under a different choice. We investigated the choices of individuals with damage to the ventral medial prefrontal cortex (VMPFC) and the lateral orbital frontal cortex (LOFC) to see whether their emotional responses were sensitive to regret. Participants made choices between gambles, each with monetary outcomes. After every choice, subjects learned the consequences of both gambles and rated their emotional response to the outcome. Normal subjects and lesion control subjects tended to make better choices and reported post-decision emotions that were sensitive to regret comparisons. VMPFC patients tended to make worse choices, and, contrary to our predictions, they reported emotions that were sensitive to regret comparisons. In contrast, LOFC patients made better choices, but reported emotional reactions that were insensitive to regret comparisons. We suggest the VMPFC is involved in the association between choices and anticipated emotions that guide future choices, while the LOFC is involved in experienced emotions that follow choices, emotions that may signal the need for behavioral change.

Intelligent management of intelligence agencies: beyond accountability ping-pong.

The intelligence community (IC) is asked to predict outcomes that may often be inherently unpredictable-and is blamed for the inevitable forecasting failures, be they false positives or false negatives. To move beyond blame games of accountability ping-pong that incentivize bureaucratic symbolism over substantive reform, it is necessary to reach bipartisan agreements on performance indicators that are transparent enough to reassure clashing elites (to whom the IC must answer) that estimates have not been politicized. Establishing such transideological credibility requires (a) developing accuracy metrics for decoupling probability and value judgments; (b) using the resulting metrics as criterion variables in validity tests of the IC's selection, training, and incentive systems; and (c) institutionalizing adversarial collaborations that conduct level-playing-field tests of clashing perspectives.

Predicting behavior in economic games by looking through the eyes of the players.

Social scientists often rely on economic experiments such as ultimatum and dictator games to understand human cooperation. Systematic deviations from economic predictions have inspired broader conceptions of self-interest that incorporate concerns for fairness. Yet no framework can describe all of the major results. We take a different approach by asking players directly about their self-interest--defined as what they want to do (pleasure-maximizing options). We also ask players directly about their sense of fairness--defined as what they think they ought to do (fairness-maximizing options). Player-defined measures of self-interest and fairness predict (a) the majority of ultimatum-game and dictator-game offers, (b) ultimatum-game rejections, (c) exiting behavior (i.e., escaping social expectations to cooperate) in the dictator game, and (d) who cooperates more after a positive mood induction. Adopting the players' perspectives of self-interest and fairness permits better predictions about who cooperates, why they cooperate, and when they punish noncooperators.

Emotions and cooperation in economic games.

In this paper, we examine decisions to cooperate in economic games. We investigate which payoffs give players the greatest pleasure and whether the pleasure they feel about payoffs predicts their decisions to cooperate. To do this, we modify the ultimatum and dictator games by asking players to consider a fixed set of offers and report their preferences over all offers. Players also report the pleasure they imagine feeling from each possible payoff. Results show that players differ in the extent to which they derive pleasure from fairness or greediness. They also differ in the extent to which their choices depend on what we call "strategic" and "non-strategic" pleasure. Strategic pleasure is the expected pleasure of offers, whereas non-strategic pleasure is the pleasure of accepted payoffs. Players whose pleasure primarily depends on larger payoffs tend to make fair offers in the ultimatum game and selfish offers in the dictator game. They maximize strategic pleasure in the ultimatum game and non-strategic pleasure in the dictator game. Players who derive greater pleasure from fairness tend to act fairly in both games. These players maximize non-strategic pleasure. Brain imaging studies should address the question of whether the observed differences in pleasure and preference are systematically linked to differences in neurological activation.

The agony of victory and thrill of defeat: Mixed emotional reactions to disappointing wins and relieving losses.

Because of counterfactual comparisons, good outcomes that could have been better (i.e., disappointing wins) and bad outcomes that could have been worse (i.e., relieving losses) elicit relatively middling ratings on bipolar emotion scales. We conducted two experiments with gambles to examine whether such outcomes elicit neutral emotions, sequentially mixed emotions of positive and negative affect, or simultaneously mixed emotions. In Experiment 1, static unipolar measures of positive and negative affect revealed that disappointing wins and relieving losses elicit mixed emotions, rather than relatively neutral emotions. In Experiment 2, participants provided continuous unipolar measures of positive and negative affect by pressing one button whenever they felt good and another button whenever they felt bad. Results revealed that disappointing wins and relieving losses elicit positive and negative affect simultaneously, rather than in alternation.

Do juries meet our expectations?

Surveys of public opinion indicate that people have high expectations for juries. When it comes to serious crimes, most people want errors of convicting the innocent (false positives) or acquitting the guilty (false negatives) to fall well below 10%. Using expected utility theory, Bayes' Theorem, signal detection theory, and empirical evidence from detection studies of medical decision making, eyewitness testimony, and weather forecasting, we argue that the frequency of mistakes probably far exceeds these "tolerable" levels. We are not arguing against the use of juries. Rather, we point out that a closer look at jury decisions reveals a serious gap between what we expect from juries and what probably occurs. When deciding issues of guilt and/or punishing convicted criminals, we as a society should recognize and acknowledge the abundance of error.