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In a paper published in Management Science in 2015, Stewart, Reimers, and Harris (SRH) demonstrated that shapes of utility and probability weighting functions could be manipulated by adjusting the distributions of outcomes and probabilities on offer as predicted by the theory of decision by sampling. So marked were these effects that, at face value, they profoundly challenge standard interpretations of preference theoretic models in which such functions are supposed to reflect stable properties of individual risk preferences. Motivated by this challenge, we report an extensive replication exercise based on a series of experiments conducted as a quasi-adversarial collaboration across different labs and involving researchers from both economics and psychology. We replicate the SRH effect across multiple experiments involving changes in many design features; importantly, however, we find that the effect is also present in designs modified so that decision by sampling predicts no effect. Although those results depend on model-based inferences, an alternative analysis using a model-free comparison approach finds no evidence of patterns akin to the SRH effect. On the basis of simulation exercises, we demonstrate that the SRH effect may be a consequence of misspecification biases arising in parameter recovery exercises that fit imperfectly specified choice models to experimental data. Overall, our analysis casts the SRH effect in an entirely new light. This paper was accepted by Yuval Rottenstreich, judgment and decision making

We present a theoretical account of the origin of the shapes of utility, probability weighting, and temporal discounting functions. In an experimental test of the theory, we systematically change the shape of revealed utility, weighting, and discounting functions by manipulating the distribution of monies, probabilities, and delays in the choices used to elicit them. The data demonstrate that there is no stable mapping between attribute values and their subjective equivalents. Expected and discounted utility theories, and also their descendants such as prospect theory and hyperbolic discounting theory, simply assert stable mappings to describe choice data and offer no account of the instability we find. We explain where the shape of the mapping comes from and, in describing the mechanism by which people choose, explain why the shape depends on the distribution of gains, losses, risks, and delays in the environment. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1853 . This paper was accepted by Yuval Rottenstreich, judgment and decision making .

It is often assumed that most people are loss averse, placing more weight on losses than commensurate gains; however, some research identifies variability in loss sensitivity that reflects features of the environment. We examined this plasticity in loss sensitivity by manipulating the size and distribution of possible outcomes in a set of mixed gambles, and assessing individual stability in loss sensitivity. In each of two sessions, participants made accept-reject decisions for 64 mixed-outcome gambles. Participants were randomly assigned to conditions defined by the relative range of losses and gains (wider range of losses vs. wider range of gains), and the currency-units at stake (‘pennies’ vs. ‘pounds’). Participants showed modest but non-trivial consistency in their sensitivity to losses; though loss sensitivity also varied substantially with our manipulations. When possible gains had greater range than possible losses, most participants were loss averse; however, when possible losses had the greater range, reverse loss aversion was the norm (i.e., more weight on gains than losses). This is consistent with decision-by-sampling theory, whereby an outcome’s rank within a consideration-set determines its value, but can also be explained by the gamble’s expected-value rank within the decision-set, or by adapting aspirations to the decision-environment. Loss aversion was also reduced in the second session of decisions when the stakes had been higher in the previous session. This illustrates the influence of prior context on current sensitivity to losses, and suggests a role for idiosyncratic experiences in the development of individual differences in loss sensitivity.

How different are £0.50 and £1.50, \"a small chance\" and \"a good chance,\" or \"three months\" and \"nine months\"? Our studies show that people behave as if the differences between these values are altered by incidental everyday experiences. Preference for a £1.50 lottery rather than a £ 0.50 lottery was stronger among individuals exposed to intermediate supermarket prices than among those exposed to lower or higher prices. Preference for \"a good chance\" rather than \"a small chance\" of winning a lottery was stronger among participants who predicted intermediate probabilities of rain than among those who predicted lower or higher chances of rain. Preference for consumption in \"three months\" rather than \"nine months\" was stronger among participants who planned for an intermediate birthday than among participants who planned for a sooner or later birthday. These fluctuations directly challenge economic accounts that translate monies, risks, and delays into subjective equivalents with stable functions. The decision-by-sampling model—in which subjective values are rank positions constructed from comparisons with samples—predicts these effects and indicates a primary role for sampling in decision making.

Background A rank based social norms model predicts that drinkers’ judgements about their drinking will be based on the rank of their breath alcohol level amongst that of others in the immediate environment, rather than their actual breath alcohol level, with lower relative rank associated with greater feelings of safety. This study tested this hypothesis and examined how people judge their levels of drunkenness and the health consequences of their drinking whilst they are intoxicated in social drinking environments. Methods Breath alcohol testing of 1,862 people (mean age = 26.96 years; 61.86 % male) in drinking environments. A subset ( N  = 400) also answered four questions asking about their perceptions of their drunkenness and the health consequences of their drinking (plus background measures). Results Perceptions of drunkenness and the health consequences of drinking were regressed on: (a) breath alcohol level, (b) the rank of the breath alcohol level amongst that of others in the same environment, and (c) covariates. Only rank of breath alcohol level predicted perceptions: How drunk they felt ( b 3.78, 95 % CI 1.69 5.87), how extreme they regarded their drinking that night ( b 3.7, 95 % CI 1.3 6.20), how at risk their long-term health was due to their current level of drinking ( b 4.1, 95 % CI 0.2 8.0) and how likely they felt they would experience liver cirrhosis ( b 4.8. 95 % CI 0.7 8.8). People were more influenced by more sober others than by more drunk others. Conclusion Whilst intoxicated and in drinking environments, people base judgements regarding their drinking on how their level of intoxication ranks relative to that of others of the same gender around them, not on their actual levels of intoxication. Thus, when in the company of others who are intoxicated, drinkers were found to be more likely to underestimate their own level of drinking, drunkenness and associated risks. The implications of these results, for example that increasing the numbers of sober people in night time environments could improve subjective assessments of drunkenness, are discussed.

How we react to humanitarian crises, epidemics, and other tragic events involving the loss of human lives depends largely on the extent to which we are moved by the size of their associated death tolls. Many studies have demonstrated that people generally exhibit a diminishing sensitivity to the number of human fatalities and, equivalently, a preference for risky (vs. sure) alternatives in decisions under risk involving human losses. However, the reason for this tendency remains unknown. Here we show that the distributions of event-related death tolls that people observe govern their evaluations of, and risk preferences concerning, human fatalities. In particular, we show that our diminishing sensitivity to human fatalities follows from the fact that these death tolls are approximately power-law distributed. We further show that, by manipulating the distribution of mortality-related events that people observe, we can alter their risk preferences in decisions involving fatalities. Finally, we show that the tendency to be risk-seeking in mortality-related decisions is lower in countries in which high-mortality events are more frequently observed. Our results support a model of magnitude evaluation based on memory sampling and relative judgment. This model departs from the utility-based approaches typically encountered in psychology and economics in that it does not rely on stable, underlying value representations to explain valuation and choice, or on choice behavior to derive value functions. Instead, preferences concerning human fatalities emerge spontaneously from the distributions of sampled events and the relative nature of the evaluation process.

This chapter provides an introduction to the economic psychology of price and inflation judgements, focusing on the main findings and the more relevant psychological theories. It discusses the process of price evaluation, and focuses on theories centred on the construct of a reference price, defined as a benchmark price used in relative evaluation processes. The chapter then briefly considers how prices are evaluated according to three psychological theories (prospect theory, decision by sampling, and norm theory), which provide further insight into evaluation processes and their effects. It further takes into account the factors affecting retrieval of reference prices and briefly mentions other aspects relevant to price evaluation. People's perceptions of past and expectations of future inflation have been found to differ from official statistics, often substantially. It is important to understand how this occurs, since perceived inflation influences expected inflation, which in turn affects economic behaviour such as wage negotiations, borrowing, saving and spending.

Modulation is an indispensable component in modern communication systems and multiple phase shift keying (MPSK) is widely studied to improve the spectral efficiency. It is of great significance to study the MPSK modulations of symmetric phases in practice. Based on convolutional neural networks (CNNs), we propose a generic architecture for MPSK demodulation, referred to as CNN-MPSK. The architecture utilizes a single-layer CNN and a pooling trick to crop network parameters. In comparison with conventional coherent demodulation, the CNN-MPSK eliminates three modules, i.e., carrier multiplication, bandpass filter and sampling decision. Thus, we can avoid π-inverted phenomenon from the multiplication of two carrier waves with different phases, as the carrier multiplication is not employed. In addition, we can reduce errors introduced by sampling decision. Furthermore, we conduct bit-error-rate tests for binary-PSK, 4PSK, 8PSK, and 16PSK demodulation. Experimental results reveal that the performance of CNN-MPSK is almost the same to that of conventional coherent demodulation. However, the CNN-MPSK demodulation reduces computational complexity from O(n2) to O(n) as compared to the latter one. Additionally, the proposed scheme can be readily applied for demodulation of non-symmetric MPSK constellations that maybe distorted by linear and nonlinear impairments in communication systems.

In network real-time communication, interrupt protection control needs to be conducted for interference nodes due to the multipath interference in the link layer. However, there have always been some incorrect operations in selecting optimal nodes, such as large errors and non-optimal masking results. Therefore, an interrupt protection method for anti-interference nodes in network real-time communication based on band sampling decision filter modulation is proposed in this paper. In this method, a multipath transmission link model of network real-time communication is constructed; an impulse response function of network communication is established; copy autocorrelation processing for signals of communication node sampling is conducted to realize the transformation from time domain to frequency domain; band sampling decision filter is used to conduct noise suppression for interference nodes; an interrupt decision is conducted for filtering output signals in network real-time communication according to the least mean square error criterion, and whether to conduct interrupt mask for nodes is decided according to the threshold, so as to realize the interrupt protection control for anti-interference nodes in network real-time communication. The simulation results show that by using this method to conduct interrupt protection for communication nodes, the output bit error rate is in general lower than those got by the traditional methods, and the highest bit error rate does not exceed 0.5. It tends to be flat from − 4SNR/dB, and completely unaffected by the interference nodes after 4SNR/dB. The method proposed in this paper restrains the interference nodes, and effectively improves the anti-interference capability and the communication quality.

The ecological niche is a multi‐dimensional concept including aspects of resource use, environmental tolerance, and interspecific interactions, and the degree to which niches overlap is central to many ecological questions. Plant phenotypic traits are increasingly used as surrogates of species niches, but we lack an understanding of how key sampling decisions affect our ability to capture phenotypic differences among species. Using trait data of ecologically distinct monkeyflower (Mimulus) congeners, we employed linear discriminant analysis to determine how (1) dimensionality (the number and type of traits) and (2) variation within species influence how well measured traits reflect phenotypic differences among species. We conducted analyses using vegetative and floral traits in different combinations of up to 13 traits and compared the performance of commonly used functional traits such as specific leaf area against other morphological traits. We tested the importance of intraspecific variation by assessing how population choice changed our ability to discriminate species. Neither using key functional traits nor sampling across plant functions and organs maximized species discrimination. When using few traits, vegetative traits performed better than combinations of vegetative and floral traits or floral traits alone. Overall, including more traits increased our ability to detect phenotypic differences among species. Population choice and the number of traits used had comparable impacts on discriminating species. We addressed methodological challenges that have undermined cross‐study comparability of trait‐based approaches. Our results emphasize the importance of sampling among‐population trait variation and suggest that a high‐dimensional approach may best capture phenotypic variation among species with distinct niches. We address key sampling issues in trait‐based ecology, demonstrating that measuring more traits and considering among‐population phenotypic variation may improve our understanding of species' phenotypic dissimilarity. Sampled monkeyflower species were no more distinctive when commonly used functional traits (vs. other morphological traits) were considered. To bridge current contrasting trait‐sampling approaches, we outline in which scenarios different strategies may be most useful.