The Natural Selection of Prediction Heuristics: Anchoring and Adjustment versus Representativeness

There are several heuristics which people use in making numerical predictions and these heuristics compete for the determination of prediction output. Some of them (e.g. representativeness) lead to excessively extreme predictions while others (e.g. anchoring and adjustment) lead to regressive (and even over-regressive) predictions. In this paper we study the competition between these two heuristics by varying the representation of predictor and outcome. The results indicate that factors which facilitate reliance on representativeness (e.g. compatibility between predictor and outcome) indeed lead to an increase in extremity, while factors that facilitate reliance on anchoring and adjustment (e.g. increased salience of a potential anchor) lead to a decrease in extremity.     

Discrepancy in internalized values of workers in extreme situations: Consequences for soldiers and risk for the organization

IntroductionIn the present paper, we address the double-edged role of values among Army personnel and how they can be used in information warfare. We suggest that value discrepancy may impact the agent's trust and consequently, affect behaviour and ultimately destabilize the organization.ObjectivesBased on cognitive dissonance theory and the meaning maintenance model, we hypothesized that exposing trained soldiers to information which confronts their core values and involves both their peers and their institution could promote an overwhelming reaction that could finally alter their trust in the organization.MethodTo examine the proposed model, interviews were conducted with 38 active-duty soldiers that permits to stress the importance of values for their personal life. To complete the interviews, a staged observation was led with the second set of participants (n = 21). Its goal was to evaluate the soldiers’ reaction to a message aiming at their values.ResultsQualitative and quantitative analysis of the Interviews confirmed both the importance of the said values and stressed the ambivalence toward their institution. The results of the staged observation suggested that a low credibility source of information aiming at their central values could lead to a direct emotional reaction, that could potentially lead to a decrease in institutional trust, and therefore ultimately in the organisation performance.ConclusionThe discussion focuses on the consequences of this mechanism, the fundamental necessity for the organization of taking this process into account. Finally, we suggest ways to cope with this risk which can alter strategy deployment and goal achievement.     

The influence of system transparency on trust: Evaluating interfaces in a highly automated vehicle

Previous studies indicate that, if an automated vehicle communicates its system status and intended behaviour, it could increase user trust and acceptance. However, it is still unclear what types of interfaces will better portray this type of information. The present study evaluated different configurations of screens comparing how they communicated the possible hazards in the environment (e.g. vulnerable road users), and vehicle behaviours (e.g. intended trajectory). These interfaces were presented in a fully automated vehicle tested by 25 participants in an indoor arena. Surveys and interviews measured trust, usability and experience after users were driven by an automated low-speed pod. Participants experienced four types of interfaces, from a simple journey tracker to a windscreen-wide augmented reality (AR) interface which overlays hazards highlighted in the environment and the trajectory of the vehicle. A combination of the survey and interview data showed a clear preference for the AR windscreen and an animated representation of the environment. The trust in the vehicle featuring these interfaces was significantly higher than pretrial measurements. However, some users questioned if they want to see this information all the time. One additional result was that some users felt motion sick when presented with the more engaging content. This paper provides recommendations for the design of interfaces with the potential to improve trust and user experience within highly automated vehicles.     

Evaluating agents’ trustworthiness within virtual societies in case of no direct experience

A great deal of effort has been made to introduce trust models to assess trustworthiness within virtual societies. The great majority of them makes extensive use of direct experience as the main source of information, considering recommendation/reputation and inferential processes just later, as a secondary mechanism to refine trust assessment. In this kind of networks, unfortunately, direct experience might not always represent the best solution to assess trustworthiness. In fact, their highly dynamic structure promotes an increase of the average number of interconnections among agents. This in turn negatively affects the degree of knowledge the agents possess about each specific individual, i.e. direct experience. To date, however, it has not been said much about how to face these situations.It is fundamental to find an effective approach for trust assessment even in lack of direct experience, which is the central focus of this research. By the means of a multi-agent social simulation, we consider the situation in which an agent can just access indirect knowledge for trust assessment, namely recommendations of specific individuals or whole categories of individuals. Then, we compare the efficiency of these two approaches in order to identify when it is more convenient to rely on the first or on the second one. As expected, our results confirm that the dynamic nature of these networks strongly affects the role of categories. We modeled this feature introducing the “turnover” in the simulations, whereby the higher is the turnover the more convenient it is relying on categories. Besides this confirmatory result, our simulations highlight the higher degree of robustness of categories in the presence of unreliable recommenders. Such a result is even more significant if there is no available information about how reliable the recommenders are.The results we obtained are in accordance with the current literature and can be of important interest for the development of this sector.     

Lasso回归：从解释到预测

传统的最小二乘回归法关注于对当前数据集的准确估计, 容易导致模型的过拟合, 影响模型结论的可重复性。随着方法学领域的发展, 涌现出的新兴统计工具可以弥补传统方法的局限, 从过度关注回归系数值的解释转向提升研究结果的预测能力也愈加成为心理学领域重要的发展趋势。Lasso方法通过在模型估计中引入惩罚项的方式, 可以获得更高的预测准确度和模型概化能力, 同时也可以有效地处理过拟合和多重共线性问题, 有助于心理学理论的构建和完善。     

Postdecision consolidation in a trend prediction task

To be able to learn from experience it is necessary to correctly apprehend experienced feedback and the situation in which it is provided. The results indicate how post-decision consolidation in complex domains may affect learning. The problem may be particularly pertinent in recurrent decision making where considerable risk is involved. The study explores the changes in aspect (signal) importance from pre- to postdiction as a function of outcome information. By postdiction we mean the remembering of an earlier prediction (cf. Hawkins & Hastie, 1990). Subjects were asked to decide on which of four alternative future price developments would follow a historical price trajectory for different commodities, and to rate the importance of each of the chosen alternative's corresponding aspects. The subjects revealed a bias in their support ratings of aspects--seeing support in aspects that traditionally (by themselves and in many contexts) would be seen as neutral or even counter-indicative of the alternative chosen. After an intermission, the subjects were also given information about what was indicated to be the actual development of the market. One group was told that their decisions were correct (irrespective of what the decisions were), another group that they were incorrect but close, a third group that they were incorrect by far, while a fourth group served as a control. Following this information the subjects were again asked to judge the importance of the aspects for their own prior decision on the most likely future development. The results indicated that outcome feed-back had an effect on post decision restructuring of facts. Subjects in the correct condition showed an average consolidation that increased the support, while the wrong conditions lead to negative consolidation (in retrospect indicating that they never found as much support for their decision in the past as they actually did). Thus, in a choice between consolidating their own initial prediction and the price trajectory they would have to live with, the decision makers consolidated the outcome. Therefore, the results of the study were related to the hindsight bias phenomenon (Fischhoff, 1975) and to Kahneman and Miller's (1986) mutability concept.     

Predicting elections from the most important issue: A test of the take‐the‐best heuristic

We used the take‐the‐best heuristic to develop a model to forecast the popular two‐party vote shares in U.S. presidential elections. The model draws upon information about how voters expect the candidates to deal with the most important issue facing the country. We used cross‐validation to calculate a total of 1000 out‐of‐sample forecasts, one for each of the last 100 days of the ten U.S. presidential elections from 1972 to 2008. Ninety‐seven per cent of forecasts correctly predicted the winner of the popular vote. The model forecasts were competitive compared to forecasts from methods that incorporate substantially more information (e.g., econometric models and the Iowa Electronic Markets). The purpose of the model is to provide fast advice on which issues candidates should stress in their campaign. Copyright © 2010 John Wiley & Sons, Ltd.