Context and uncertainty in decisions from experience

From the moment we wake up each morning, we are faced with countless choices. Should we press snooze on our alarm? Have toast or cereal for breakfast? Bring an umbrella? Agree to work on that new project? Go to the gym or eat a whole pizza while watching Netflix? The challenge when studying decision-making is to collapse these diverse scenarios into feasible experimental methods. The standard theoretical approach is to represent options using outcomes and probabilities and this has provided a rationale for studying decisions using gambling tasks. These tasks typically involve repeated choices between a single pair of options and outcomes that are determined probabilistically. Thus, the two sections in this thesis ask a simple question: are we missing something by using pairs of options that are divorced from the context in which we make choices outside the psychology laboratory? The first section focuses on the impact of extreme outcomes within a decision context. Chapter 2 addresses whether there is a rational explanation for why these outcomes appear in decisions from experience and numerous other cognitive domains. Chapters 3-5 describe six experiments that distinguish between plausible theories based on whether they measure extremity as categorical, ordinal, or continuous; whether extremity refers to the centre, the edges, or neighbouring outcomes; whether outcomes are represented as types or tokens; and whether extreme outcomes are defined using temporal or distributional characteristics. In the second section, we shift our focus to how people perceive uncertainty. We examine a distinction between uncertainty that is attributed to inadequate knowledge and uncertainty that is attributed to an inherently random process. Chapter 6 describes three experiments that examine whether allowing participants to map their uncertainty onto observable variability leads them to perceive it as potentially resolvable rather than purely stochastic. We then examine how this influences whether they seek additional information. In summary, the experiments described in these two sections demonstrate the importance of context and uncertainty in understanding how we make decisions

When the appeal of a dominant leader is greater than a prestige leader

Across the globe we witness the rise of populist authoritarian leaders who are overbearing in their narrative, aggressive in behavior, and often exhibit questionable moral character. Drawing on evolutionary theory of leadership emergence, in which dominance and prestige are seen as dual routes to leadership, we provide a situational and psychological account for when and why dominant leaders are preferred over other respected and admired candidates. We test our hypothesis using three studies, encompassing more than 140,000 participants, across 69 countries and spanning the past two decades. We find robust support for our hypothesis that under a situational threat of economic uncertainty (as exemplified by the poverty rate, the housing vacancy rate, and the unemployment rate) people escalate their support for dominant leaders. Further, we find that this phenomenon is mediated by participants’ psychological sense of a lack of personal control. Together, these results provide large-scale, globally representative evidence for the structural and psychological antecedents that increase the preference for dominant leaders over their prestigious counterparts

Communicating Uncertainty: The Role of Communication Format in Maximising Understanding and Maintaining Credibility

This thesis investigates the effect of communication format on the understanding of uncertainty communications and considers the implications of these findings for a communicator’s perceived credibility. The research compares five formats: verbal probability expressions (VPEs; e.g., ‘unlikely’); numerical expressions – point (e.g., ‘20% likelihood’) and range estimates (e.g., ‘10–30% likelihood’); and mixed expressions in two orders (verbal-numerical, e.g., ‘unlikely [20% likelihood]’ and numerical-verbal format, e.g., ‘20% likelihood [unlikely]’). Using the ‘which-outcome’ methodology, we observe that when participants are asked to estimate the probability of the outcome of a natural hazard that is described as ‘unlikely’, the majority indicate outcomes with a value exceeding the maximum value shown, equivalent to a 0% probability. Extending this work to numerical and mixed formats, we find that 0% interpretations are also given to communications using a verbal-numerical format (Chapter 2). If ‘unlikely’ is interpreted as referring to events which will never occur, there could be implications for a communicator’s perceived credibility should an ‘unlikely’ event actually occur. In the low probability domain, we find a communicator who uses a verbal format in their prediction is perceived as less credible and less correct than one who uses a numerical format. However, in the high probability domain (where a ‘likely’ event does not occur) such an effect of format is not consistently observed (Chapter 3). We suggest ‘directionality–outcome congruence’ can explain these findings. For example, the negatively directional term ‘unlikely’ led to harsher ratings because the outcome was counter to the original focus of the prediction (i.e., on its non-occurrence). Comparing communications featuring positively and negatively directional VPEs, we find that communicators are perceived as less credible and less correct given directionality–outcome incongruence (Chapter 4). Our findings demonstrate the influence of pragmatics on (a) the understanding of uncertainty communications and (b) perceived communicator credibility

Communicating uncertainty using words and numbers

Life in an increasingly information-rich but highly uncertain world calls for an effective means of communicating uncertainty to a range of audiences. Senders prefer to convey uncertainty using verbal (e.g. likely) rather than numeric (e.g. 75% chance) probabilities, even in consequential domains such as climate science. However, verbal probabilities can convey something other than uncertainty, and senders may exploit this. For instance, senders can maintain credibility after making erroneous predictions. While verbal probabilities afford ease of expression, they can be easily misunderstood, and the potential for miscommunication is not effectively mitigated by assigning (imprecise) numeric probabilities to words. When making consequential decisions, recipients prefer (precise) numeric probabilities

Bayesian networks for the multi-risk assessment of road infrastructure

The purpose of this study is to develop a methodological framework for the multi-risk assessment of road infrastructure systems. Since the network performance is directly linked to the functional states of its physical elements, most efforts are devoted to the derivation of fragility functions for bridges exposed to potential earthquake, flood and ground failure events. Thus, a harmonization effort is required in order to reconcile fragility models and damage scales from different hazard types. The proposed framework starts with the inventory of the various hazard-specific damaging mechanisms or failure modes that may affect each bridge component (e.g. piers, deck, bearings). Component fragility curves are then derived for each of these component failure modes, while corresponding functional consequences are proposed in a component-level damage-functionality matrix, thanks to an expert-based survey. Functionality-consistent failure modes at the bridge level are then assembled for specific configurations of component damage states. Finally, the development of a Bayesian Network approach enables the robust and efficient derivation of system fragility functions that (i) directly provide probabilities of reaching functionality losses and (ii) account for multiple types of hazard loadings and multi-risk interactions. At the network scale, a fully probabilistic approach is adopted in order to integrate multi-risk interactions at both hazard and fragility levels. A temporal dimension is integrated to account for joint independent hazard events, while the hazard-harmonized fragility models are able to capture cascading failures. The quantification of extreme events cannot be achieved by conventional sampling methods, and therefore the inference ability of Bayesian Networks is investigated as an alternative. Elaborate Bayesian Network formulations based on the identification of link sets are benchmarked, thus demonstrating the current computational difficulties to treat large and complex systems

Falling Back on Numbers: When Preference for Numerical Product Information Increases after a Personal Control Threat

Despite the ubiquity of numerical information in consumers’ lives, prior research has provided limited insights to marketers about when numerical information exerts greater impact on decisions. This study offers evidence that judgments involving numerical information can be affected by consumers’ sense of personal control over the environment. A numerical attribute’s format communicates the extent to which the magnitude of a benefit is predictable (Study 1a), such that people who experience a control threat and want to see their external environment as predictable (Study 1b) rely on point value (vs. range) information as a general signal that the environment is predictable (Study 2). A personal control threat changes consumers’ preferences as a function of whether the numerical information appears as a point value or a range (Studies 3–4). This heightened focus on format may lessen the impact of a product benefit’s predicted magnitude, if a lower magnitude is specified in a more precise format (Study 5). Study 6 provides first evidence that the interactive effect of personal control levels and numerical formats can affect consequential choices

Arithmetic computation with probability words and numbers

Probability information is regularly communicated to experts who must fuse multiple estimates to support decision-making. Such information is often communicated verbally (e.g., “likely”) rather than with precise numeric (point) values (e.g., “.75”), yet people are not taught to perform arithmetic on verbal probabilities. We hypothesized that the accuracy and logical coherence of averaging and multiplying probabilities will be poorer when individuals receive probability information in verbal rather than numerical point format. In four experiments (N = 213, 201, 26, and 343, respectively), we manipulated probability communication format between-subjects. Participants averaged and multiplied sets of four probabilities. Across experiments, arithmetic accuracy and coherence was significantly better with point than with verbal probabilities. These findings generalized between expert (intelligence analysts) and non-expert samples and when controlling for calculator use. Experiment 4 revealed an important qualification: whereas accuracy and coherence were better among participants presented with point probabilities than with verbal probabilities, imprecise numeric probability ranges (e.g., “.70 to .80”) afforded no computational advantage over verbal probabilities. Experiment 4 also revealed that the advantage of the point over the verbal format is partially mediated by strategy use. Participants presented with point estimates are more likely to use mental computation than guesswork, and mental computation was found to be associated with better accuracy. Our findings suggest that where computation is important, probability information should be communicated to end users with precise numeric probabilities

Judgmental forecasting: Factors affecting lay people's expectations of inflation

In this thesis, laypeople’s judgmental forecasting about inflation is reviewed and experimentally explored in six chapters. Inflation is defined as the Consumer Price Index (CPI) across the whole thesis. In Chapter 1, I review work on the formation of inflation expectations, drawing mainly from the economic literature. In Chapter 2, I review research on judgmental forecasting, drawing mainly from the literature in cognitive psychology and management science. In Chapter 3, three experiments are presented that were designed to determine how and when people employ internal information of experienced price changes to form inflation expectations. In Chapter 4, three experiments are used to investigate the effects of providing within-series and across-series historical information (inflation rates, interest rates and unemployment rates) on inflation expectations. In Chapter 5, two experiments are reported that examine how training using simple outcome feedback increases the accuracy of inflation judgments and improves the calibration of confidence in those judgments. Chapter 6 reports experiments designed to examine the effects of using different elicitation methods (point forecasts, interval forecasts and density forecasts) on the accuracy of inflation judgments. Chapter 7 is a concluding chapter that summarises findings from these experiments and suggests avenues for future work

What is a “likely” amount? Representative (modal) values are considered likely even when their probabilities are low

Research on verbal probabilities and standard scales issued by national and international authorities suggest that only events with probabilities above 60% should be labelled “likely”. We find, however, that when people apply this term to continuous variables, like expected costs, it describes the most likely (modal) outcome or interval, regardless of actual probabilities, which may be quite small. This was demonstrated in six studies in which lay participants (N = 2,228) were shown probability distributions from various domains and asked to generate or to select “likely” outcome intervals. Despite having numeric and graphically displayed information available, participants judged central, low-probability segments as “likely” (as opposed to equal or larger segments in the tails) and subsequently overestimated the chances of these outcomes. We conclude that high-probability interpretations of “likely” are only valid for binary outcomes but not for distributions of graded variables or multiple outcomes