Dimensionality of risk perception : factors affecting consumer understanding and evaluation of financial risk

This article describes two studies of the factors affecting consumer understanding of financial risk. The first study investigated factors affecting people's perception and comprehension of information about the risks related to retirement investments. First, we asked respondents to list possible risk factors related to investment in a pension plan. Then we obtained ratings of different factors (e.g., the perceived level of knowledge about an investment) that could affect perception of the risk of financial products and retirement investment decisions. Finally, we asked the subjects to rate 11 different descriptions presenting risk information about the same financial product. The risk information framing that received highest rating presented risk as variation between minimum and maximum values with an average in between. The second study demonstrated the risk framing that received highest ranking also prompted more stable risk preferences over a 3-month testing period in comparison to standard measures of risk aversion. Thus, the second study corroborated the importance of the findings in the first study and also indicated that, although people can exhibit stable risk preferences if we ask them the right questions, these preferences were very specific to the risk domain

Risk preference discrepancy : a prospect relativity account of the discrepancy between risk preferences in laboratory gambles and real world investments

In this article, we presented evidence that people are more risk averse when investing in financial products in the real world than when they make risky choices between gambles in laboratory experiments. In order to provide an account for this discrepancy, we conducted experiments, which showed that the range of offered investment funds that vary in their riskreward characteristics had a significant effect on the distribution of hypothetical funds to those products. We also showed that people are able to use the context provided by the choice set in order the make relative riskiness judgments for investment products. This context dependent relativistic nature of risk preferences is proposed as a plausible explanation of the risk preference discrepancy between laboratory experiments and real-world investments. We also discuss other possible theoretical interpretations of the discrepancy

Relativistic financial decisions : context effects on retirement saving and investment risk preferences

We report a study of the effects the choice set on financial decision making related to retirement savings and risky investment. The participants were presented with either a full range of choice options or a limited subset of the feasible options. The choices of saving and risk are affected by the position of each option in the range of presented options. This result demonstrated that the range of the options offered as possible saving rates and levels of investment risk influences decisions about saving and risk. The study was conducted on a sample of working people, and we controlled whether the participants can financially afford in their real life the decisions taken in the test. In addition, various measures of risk aversion did not account for the risk taken in each condition. Surprisingly, only the simplest and most direct risk preference measure was a significant predictor of the responses within a particular choice set context, although the actual choices were still very much influenced by the range. Thus, the results reported here suggest that financial judgments and choices are relative, which corroborates, in an important practical domain, previous related work with abstract gambles and hypothetical risky investments

Facing up to the uncertainties of Covid-19

The human tendency to impose a single interpretation in ambiguous situations carries huge dangers in addressing COVID-19. We need to search actively for multiple interpretations, and governments need to choose policies that are robust if their preferred theory turns out to be wrong, argues Nick Chater

Algorithmic Identification of Probabilities

TThe problem is to identify a probability associated with a set of natural numbers, given an infinite data sequence of elements from the set. If the given sequence is drawn i.i.d. and the probability mass function involved (the target) belongs to a computably enumerable (c.e.) or co-computably enumerable (co-c.e.) set of computable probability mass functions, then there is an algorithm to almost surely identify the target in the limit. The technical tool is the strong law of large numbers. If the set is finite and the elements of the sequence are dependent while the sequence is typical in the sense of Martin-L\"of for at least one measure belonging to a c.e. or co-c.e. set of computable measures, then there is an algorithm to identify in the limit a computable measure for which the sequence is typical (there may be more than one such measure). The technical tool is the theory of Kolmogorov complexity. We give the algorithms and consider the associated predictions.Comment: 19 pages LaTeX.Corrected errors and rewrote the entire paper. arXiv admin note: text overlap with arXiv:1208.500

The Bayesian sampler : generic Bayesian inference causes incoherence in human probability

Human probability judgments are systematically biased, in apparent tension with Bayesian models of cognition. But perhaps the brain does not represent probabilities explicitly, but approximates probabilistic calculations through a process of sampling, as used in computational probabilistic models in statistics. Naïve probability estimates can be obtained by calculating the relative frequency of an event within a sample, but these estimates tend to be extreme when the sample size is small. We propose instead that people use a generic prior to improve the accuracy of their probability estimates based on samples, and we call this model the Bayesian sampler. The Bayesian sampler trades off the coherence of probabilistic judgments for improved accuracy, and provides a single framework for explaining phenomena associated with diverse biases and heuristics such as conservatism and the conjunction fallacy. The approach turns out to provide a rational reinterpretation of “noise” in an important recent model of probability judgment, the probability theory plus noise model (Costello & Watts, 2014, 2016a, 2017; Costello & Watts, 2019; Costello, Watts, & Fisher, 2018), making equivalent average predictions for simple events, conjunctions, and disjunctions. The Bayesian sampler does, however, make distinct predictions for conditional probabilities and distributions of probability estimates. We show in 2 new experiments that this model better captures these mean judgments both qualitatively and quantitatively; which model best fits individual distributions of responses depends on the assumed size of the cognitive sample

Sequence effects in categorization of simple perceptual stimuli

Categorization research typically assumes that the cognitive system has access to a (more or less noisy) representation of the absolute magnitudes of the properties of stimuli and that this information is used in reaching a categorization decision. However, research on identification of simple perceptual stimuli suggests that people have very poor representations of absolute magnitude information and that judgments about absolute magnitude are strongly influenced by preceding material. The experiments presented here investigate such sequence effects in categorization tasks. Strong sequence effects were found. Classification of a borderline stimulus was more accurate when preceded by a distant member of the opposite category than by a distant member of the same category. It is argued that this category contrast effect cannot be accounted for by extant exemplar or decision-bound models of categorization. The effect suggests the use of relative magnitude information in categorization. A memory and contrast model illustrates how relative magnitude information may be used in categorization

The under-appreciated drive for sense-making

This paper draws attention to a powerful human motive that has not yet been incorporated into economics: the desire to make sense of our immediate experience, our life, and our world. We propose that evolution has produced a ‘drive for sense-making’ which motivates people to gather, attend to, and process information in a fashion that augments, and complements, autonomous sense-making. A large fraction of autonomous cognitive processes are devoted to making sense of the information we acquire: and they do this by seeking simple descriptions of the world. In some situations, however, autonomous information processing alone is inadequate to transform disparate information into simple representations, in which case, we argue, the drive for sense-making directs our attention and can lead us to seek out additional information. We propose a theoretical model of sense-making and of how it is traded off against other goals. We show that the drive for sense-making can help to make sense of a wide range of disparate phenomena, including curiosity, boredom, ‘flow’, confirmation bias and information avoidance, esthetics (both in art and in science), why we care about others’ beliefs, the importance of narrative and the role of ‘the good life’ in human decision making