Can Conspiracy Beliefs Be Beneficial? Longitudinal Linkages Between Conspiracy Beliefs, Anxiety, Uncertainty Aversion, and Existential Threat

Research suggests that conspiracy beliefs are adopted because they promise to reduce anxiety, uncertainty, and threat. However, little research has investigated whether conspiracy beliefs actually fulfill these promises. We conducted two longitudinal studies ( NStudy 1 = 405, NStudy 2 = 1,012) to examine how conspiracy beliefs result from, and in turn influence, anxiety, uncertainty aversion, and existential threat. Random intercept cross-lagged panel analyses indicate that people who were, on average, more anxious, uncertainty averse, and existentially threatened held stronger conspiracy beliefs. Increases in conspiracy beliefs were either unrelated to changes in anxiety, uncertainty aversion, and existential threat (Study 2), or even predicted increases in these variables (Study 1). In both studies, increases in conspiracy beliefs predicted subsequent increases in conspiracy beliefs, suggesting a self-reinforcing circle. We conclude that conspiracy beliefs likely do not have beneficial consequences, but may even reinforce the negative experience of anxiety, uncertainty aversion, and existential threat.

Stock markets' reaction to COVID-19: the joint impact of uncertainty avoidance culture and government response–evidence from emerging countries

PurposeThe authors examine whether the uncertainty avoidance culture and the stringency of government response play a role in shaping the stock market's response to coronavirus disease 2019 (COVID-19). The authors find that investors' response to the pandemic will not only depend on their instinct of uncertainty aversion but also on their expectation about the effectiveness of the government measures. The uncertainty avoidance culture amplifies the irrational actions of investors. However, harsh government responses will weaken this effect. Harsh government responses also send a negative signal to the market about the extent of the pandemic and the economic damage caused by anti-COVID measures. Governments need to be balanced in imposing anti-COVID measurements to preserve market confidence.Design/methodology/approachIn this article, the authors investigate whether the stock market volatility of emerging countries is simultaneously driven by two factors: the uncertainty-aversion culture of investors in a country and the stringency of the government's response to the pandemic. The authors conduct an empirical study on a sample of 20 emerging countries during the period from January 2020 to March 2021.FindingsThe authors find that the national-level uncertainty aversion amplifies the irrational actions of investors during the period of crisis. However, harsh government responses will weaken this effect. The authors’ findings show evidence that investors' response to the pandemic will not only depend on their instinct of uncertainty aversion but also on their expectation about the effectiveness of the government measures. Although harsh government responses can stabilize the investors' sentiment in countries with high levels of uncertainty aversion, they also send a negative signal to the market about the extent of the pandemic as well as the economic damage caused by anti-COVID measures.Originality/valueFirst, the study’s results complement evidence from existing studies on the effect of uncertainty avoidance culture in determining stock market responses to COVID-19. Second, an important difference from previous studies, this paper adds to the behavioral finance literature by showing that investors' investment decisions in the face of economic uncertainty are not driven solely by their cultural values but also by their expectation about the effectiveness of the government policy. During a crisis, when the market has neither rational information nor adequate experience to forecast the future, the government must play an important role in stabilizing investors' sentiment and reactions.

Computationally-defined markers of uncertainty aversion predict trajectories of perceived threat and anxiety during a global pandemic

Exposure to stressful life events involving threat and uncertainty often results in the development of anxiety. However, the factors that confer risk and resilience for anxiety following real world stress at a computational level remain unclear. We identified core components of uncertainty aversion moderating response to stress posed by the COVID-19 pandemic derived from computational modelling of decision making. Using both cross-sectional and longitudinal analyses, we investigated both immediate effects at the onset of the stressor, as well as medium-term changes in response to persistent stress. 479 subjects based in the United States completed a decision-making task measuring risk aversion, loss aversion, and ambiguity aversion in the early stages of the pandemic (March 2020). Self-report measures targeting threat perception, anxiety, and avoidant behavior in response to the pandemic were collected at the same time point and 8 weeks later (May 2020). Cross-sectional analyses indicated that higher risk aversion predicted higher perceived threat from the pandemic, and ambiguity aversion for guaranteed gains predicted perceived threat and pandemic-related anxiety. In longitudinal analyses, ambiguity aversion for guaranteed gains predicted greater increases in perceived infection likelihood. Together, these results suggest that individuals who have a low-level aversion towards uncertainty show stronger negative reactions to both the onset and persistence of real-life stress.

Revisiting Ellsberg’s and Machina’s Paradoxes: A Two-Stage Evaluation Model Under Ambiguity

In this paper, a two-stage evaluation (TSE) model for decision making under ambiguity is proposed. Events in state space are classified into risky and ambiguous events, which correspond to different types of uncertainty generated by different sources. In this TSE model, uncertainty of two different types are evaluated by decision maker (DM) in different stages. In the first stage, DM evaluates more uncertain consequences of an act locally by applying local subjective expected utility (SEU) models, which are then embedded into the second-stage evaluation based on SEU defined globally over all events. To axiomatize such a model, the small domain SEU over risky acts is extended to both risky and nonrisky (ambiguous) acts. When evaluating a risky act, TSE model reduces to Savage’s SEU with one stage. When evaluating an ambiguous act, local SEU with a different uncertainty aversion defined on ambiguous events gives TSE model some flexibility in describing preferences. It can be shown that TSE model can accommodate Ellsberg’s paradoxes and Machina’s paradoxes in the literature. When applied to portfolio selection problem, TSE model enjoys some nice properties other models do not have. This paper was accepted by Manel Baucells, decision analysis.

Influence of Naturalistic, Emotional Context and Intolerance of Uncertainty on Arousal-Mediated Biases in Episodic Memory

Threat-related arousal is known to distort memory, biasing individuals towards perceptual details and away from contextual details. This work has mainly been conducted in laboratory settings, limiting the application of findings to real-world experiences. To test how threat-related arousal influences multi-featural memory for complex events, participants navigated an immersive haunted house while physiological arousal data was collected and later, recalled memories for the event after a 1-week delay. We found that threat-related arousal resulted in relatively fewer remembered events, but enhanced recall of perceptual details for events that were remembered. Further, the relationship between physiological arousal and perceptual bias was impaired in individuals with high intolerance of uncertainty, suggesting that uncertainty aversion may result in a generalization of threat-related perceptual biases to mundane events. These findings support a model by which heart rate and individual differences in uncertainty aversion interact to shape how threatening events are recorded in long-term memory.

Preferences and Production under Uncertainty

The Arrow-Savage-Debreu formalism (state space, consequence space, acts) for modelling a stochastic decision is introduced. Preferences over stochastic outcomes framed as maps (acts) from the state space to the consequence space are studied and related to nonstochastic preference structures. Distance function representations of preferences are developed and their superdifferential correspondences are shown to define subjective probability measures. Structural restrictions including uncertainty aversion, constant absolute uncertainty aversion, and constant relative uncertainty aversion are examined and related to parallel restrictions for nonstochastic preference or production structures. A model of a stochastic technology that has the nonstochastic production model as a special case is introduced, and distance function representations of it are discussed. Structural assumptions on the stochastic technology are discussed.

The Reflection Effect in Memory-Based Decisions

Previous research has indicated a bias in memory-based decision-making, with people preferring options that they remember better. However, the cognitive mechanisms underlying this memory bias remain elusive. Here, we propose that choosing poorly remembered options is conceptually similar to choosing options with uncertain outcomes. We predicted that the memory bias would be reduced when options had negative subjective value, analogous to the reflection effect, according to which uncertainty aversion is stronger in gains than in losses. In two preregistered experiments ( N = 36 each), participants made memory-based decisions between appetitive and aversive stimuli. People preferred better-remembered options in the gain domain, but this behavioral pattern reversed in the loss domain. This effect was not related to participants’ ambiguity or risk attitudes, as measured in a separate task. Our results increase the understanding of memory-based decision-making and connect this emerging field to well-established research on decisions under uncertainty.

Financial Markets as Financial Systems: A Philosophical Dialogue between Offshore Tax Haven Islands and Systemic Failures

This paper deals with financial markets as forms of financial systems. For the first time theorised under a Luhmannian paradigm, markets are conceptualised as financial systems informed by four main structures: risk, uncertainty, competition, and financial innovation. According to Frank Knight, risk is a measurable entity and can always be calculated, but uncertainty is unmeasurable. Nonetheless, uncertainty constitutes the main catalyst for money creation processes in modern economies. In other words, it underpins profit rather than undermining it. For this reason, the modern financial actor is destined to fail. It must deal with uncertainty if it wants to make a profit, but at the same time it can experience only uncertainty, due to its unmeasurable nature. This has caused a profound dilemma between ‘uncertainty-aversion’ paradigms and self-regulation approaches. The paper argues for a mixed system of hybrid regulation, on the one hand to save the environments of tax haven islands via more transparent policies to tax those same offshore companies, and on the other to promote a system of co-operation between financial operators and the state. This is the only way to promote auto-regeneration processes for the whole financial system, because the system itself has already been programmed to fail, and systemic failures produce contagious effects, showing a deeper interconnection of financial systems tout court as well suggesting a cure via a spontaneous mechanism of auto-regeneration, or autopoiesis.