决策过程中的建议采纳

建议采纳是指决策者参考他人建议并形成最终决策的过程。在过去的20年中,建议采纳研究集中探讨了三方面问题：（1）评判者多大程度上采纳了他人建议;（2）他人建议对决策质量的提升作用;（3）建议者和评判者在决策中的信心。本文首先介绍了建议采纳研究的实验范式,并从测量方法和研究成果两方面对上述三个问题进行回顾。未来的研究应注意丰富“建议”的外延、关注“建议者”角色、拓展决策任务、并探讨情绪在建议采纳过程中的作用。     

Advice as a form of social influence: Informational motives and the consequences for accuracy

In this article, we ask how well people fulfill informational motives by using the judgments of others. We build on advice‐taking research from the judgment and decision making literature, which has developed a distinct paradigm to test how accurately people incorporate information from others. We use a literature review to show that people have mixed success in fulfilling informational motives—they increase their accuracy through the use of advice, but not as much as they could. We develop insights about how people perceive advisors and try to pursue advice—and where their perceptions may lead them astray. We conclude by proposing that future work further investigate the reasons people fail to use advice by building on the current advice taking paradigm used in judgment and decision making, but with a richer understanding of advice taking as a dynamic process that often entails complex decisions and normative motives.     

当局者迷,旁观者清?自我-他人决策的理性差异及其机制

生活充满抉择,由于知识经验的局限,人们常需寻求他人建议,抑或直接请他人代己决策.诸多研究探讨了自我决策、向他人建议,以及代他人决策之间的差异.探究这种差异的动因之一在于考察何种条件下的决策更优或更“理性”.以往研究表明,自我决策或他人决策(向他人建议或代他人决策)均有可能更易违背理性决策原则,隐含着他人决策优于自我决策的“当局者迷,旁观者清”这一传统智慧有一定的边界条件.研究者一般从认知(建构水平理论)、情绪(类型和卷入度),以及动机(调节聚焦理论)三种视角对自我-他人决策差异进行解释.本文作者提出基于理由的决策(reason-based account)假设来解释自我-他人决策在理性程度上的差异.未来研究可从决策过程及脑机制上深入考察自我-他人决策差异及其机制.     

Interaction with Others Increases Decision Confidence but Not Decision Quality: Evidence against Information Collection Views of Interactive Decision Making

We present three studies of interactive decision making, where decision makers interact with others before making a final decision alone. Because the theories of lay observers and social psychologists emphasize the role of information collection in interaction, we developed a series of tests of information collection. Two studies with sports collection show that interaction does not increase decision accuracy or meta-knowledge (calibration or resolution). The simplest test of information collection is responsiveness - that people should respond to information against their position by modifying their choices or at least lowering their confidence. Studies using traditional scenarios from the group polarization literature show little responsiveness, and even "deviants," who interact with others who unanimously disagree with their choice, frequently fail to respond to the information they collect. The most consistent finding is that interaction increases people′s confidence in their decisions in both sports predictions and risky shift dilemmas. For predictions, confidence increases are not justified by increased accuracy. These results question theories of interaction which assume that people collect information during interaction (e.g., Persuasive Arguments Theory). They also question the labeling of previous results as "shifts" or "polarization." We suggest that interaction is better understood as rationale construction than as information collection - interaction forces people to explain their choices to others, and a variety of previous research in social psychology has shown that explanation generation leads to increased confidence. In Study 3, we provide a preliminary test of rationale construction by showing that people increase in confidence when they construct a case for their position individually, without interaction.     

Doc, what would you do if you were me? On self-other discrepancies in medical decision making

Doctors often make decisions for their patients and predict their patients' preferences and decisions to customize advice to their particular situation. We investigated how doctors make decisions about medical treatments for their patients and themselves and how they predict their patients' decisions. We also studied whether these decisions and predictions coincide with the decisions that the patients make for themselves. We document 3 important findings. First, doctors made more conservative decisions for their patients than for themselves (i.e., they more often selected a safer medical treatment). Second, doctors did so even if they accurately predicted that their patients would want a riskier treatment than the one they selected. Doctors, therefore, showed substantial self-other discrepancies in medical decision making and did not make decisions that accurately reflected their patients' preferences. Finally, patients were not aware of these discrepancies and thought that the decisions their doctors made for themselves would be similar to the decisions they made for their patients. We explain these results in light of 2 current theories of self-other discrepancies in judgment and decision making: the risk-as-feelings hypothesis and the cognitive hypothesis. Our results have important implications for research on expert decision making and for medical practice, and shed some light on the process underlying self-other discrepancies in decision making.     

The Effects of Decision Outcome Dispersion Upon Organizational Decision Making

The effects of varying decision outcome dispersion on organizational decision making were investigated under individual and group decision making conditions. Thirty-six female and pg]36 male subjects made decisions for organizational decision scenarios in which outcomes affected primarily the decision maker, people other than the decision maker, or a group of which the decision maker was a member. Subjects rated their levels of perceived risk and confidence in their decisions and made decisions within a simulated context of either a small or a large organization. Results indicated that subjects perceived significantly less risk and more confidence in their decisions when outcomes affected primarily themselves rather than others regardless of whether the decisions were made individually or by a group. Males perceived their decisions as significantly more risky than females. Induced organizational size did not significantly influence decision making.

     

Information Integration in Juror Decision Making

Research applying information integration theory to jury decision making has long assumed that people average informational scale values when making legal judgments. Although often consistent with research results, this hypothesis has never been tested in a legal context against a more general additive rule. The present paper describes two studies conducted as a critical test between these two models. Incriminating evidence and eyewitness confidence were varied in a full-factorial, within-subjects design involving a total of 131 subjects acting as mock jurors. Subject responses included eyewitness accuracy and defendant-guilt probability estimates, as well as final verdict decisions. Results strongly support an averaging model of legal decision making. Additional results concerning the influence of initial attitudes and the interrelationships between the variables considered are reported and their implications are discussed.     

Sequential sampling models of human text classification

Text classification involves deciding whether or not a document is about a given topic. It is an important problem in machine learning, because automated text classifiers have enormous potential for application in information retrieval systems. It is also an interesting problem for cognitive science, because it involves real world human decision making with complicated stimuli. This paper develops two models of human text document classification based on random walk and accumulator sequential sampling processes. The models are evaluated using data from an experiment where participants classify text documents presented one word at a time under task instructions that emphasize either speed or accuracy, and rate their confidence in their decisions. Fitting the random walk and accumulator models to these data shows that the accumulator provides a better account of the decisions made, and a “balance of evidence” measure provides the best account of confidence. Both models are also evaluated in the applied information retrieval context, by comparing their performance to established machine learning techniques on the standard Reuters‐21578 corpus. It is found that they are almost as accurate as the benchmarks, and make decisions much more quickly because they only need to examine a small proportion of the words in the document. In addition, the ability of the accumulator model to produce useful confidence measures is shown to have application in prioritizing the results of classification decisions.     

认知闭合需要、框架效应与决策偏好

在带有模糊性的决策情境中,决策者个人的认知特征会对其判断决策产生重要影响。通过实验的方法,考察了认知闭合需要和特征框架效应对个体决策偏好的影响。93名工商管理硕士（MBA）参与了实验,研究的结果支持了本研究的3个假设,即认知闭合需要与特征框架效应不仅对被试的决策偏好存在显著的影响,而且二者还存在显著的交互作用。具体来说,研究发现,在模糊情境中：高认知闭合需要的被试偏好于立刻做出决策,而低认知闭合需要的被试偏好于暂缓做出决策;接收到正向框架信息的被试偏好于立刻做出决策,而接收到负向框架信息的被试偏好于暂缓做出决策;认知闭合需要与特征框架对被试的决策偏好还存在显著的交互作用。研究结论为根据个体认知闭合需要的水平来选拔决策者、利用框架效应来影响个体的信息加工方式进而提高决策质量提供了理论依据     

How social and non-social information influence classification decisions: A computational modelling approach

Social information such as observing others can improve performance in decision making. In particular, social information has been shown to be useful when finding the best solution on one’s own is difficult, costly, or dangerous. However, past research suggests that when making decisions people do not always consider other people’s behaviour when it is at odds with their own experiences. Furthermore, the cognitive processes guiding the integration of social information with individual experiences are still under debate. Here, we conducted two experiments to test whether information about other persons’ behaviour influenced people’s decisions in a classification task. Furthermore, we examined how social information is integrated with individual learning experiences by testing different computational models. Our results show that social information had a small but reliable influence on people’s classifications. The best computational model suggests that in categorization people first make up their own mind based on the non-social information, which is then updated by the social information.     

Consumer Trust and Advice Acceptance: The Moderating Roles of Benevolence,Expertise, and Negative Emotions

I explored advice acceptance for high‐stakes decisions (i.e., those with subjectively important and risky outcomes), focusing on the relative influence of two components of consumer trust—benevolence and expertise—as well as perceived emotional decision difficulty. Participants solicited advice from experts when their decisions were low in perceived emotional difficulty but favored the advice of predominantly benevolent providers when making highly emotionally difficult decisions. Although consumers who faced emotionally difficult decisions were willing to trade off expertise for benevolence, they did not perceive this non‐normative trade‐off to influence decision quality. Instead, the results support a “stress buffering” effect whereby consumers were more confident in the accuracy of predominantly benevolent providers’ advice.     

Decision makers calibrate behavioral persistence on the basis of time-interval experience

A central question in intertemporal decision making is why people reverse their own past choices. Someone who initially prefers a long-run outcome might fail to maintain that preference for long enough to see the outcome realized. Such behavior is usually understood as reflecting preference instability or self-control failure. However, if a decision maker is unsure exactly how long an awaited outcome will be delayed, a reversal can constitute the rational, utility-maximizing course of action. In the present behavioral experiments, we placed participants in timing environments where persistence toward delayed rewards was either productive or counterproductive. Our results show that human decision makers are responsive to statistical timing cues, modulating their level of persistence according to the distribution of delay durations they encounter. We conclude that temporal expectations act as a powerful and adaptive influence on people's tendency to sustain patient decisions.     

Receiving other people’s advice: Influence and benefit

Seeking advice is a basic practice in making real life decisions. Until recently, however, little attention has been given to it in either empirical studies or theories of decision making. The studies reported here investigate the influence of advice on judgment and the consequences of advice use for judgment accuracy. Respondents were asked to provide final judgments on the basis of their initial opinions and advice presented to them. The respondents’ weighting policies were inferred. Analysis of the these policies show that (a) the respondents tended to place a higher weight on their own opinion than on the advisor’s opinion (the self/other effect); (b) more knowledgeable individuals discounted the advice more; (c) the weight of advice decreased as its distance from the initial opinion increased; and (d) the use of advice improved accuracy significantly, though not optimally. A theoretical framework is introduced which draws in part on insights from the study of attitude change to explain the influence of advice. Finally the usefulness of advice for improving judgment accuracy is considered.     

Biased forecasting of postdecisional affect

Although anticipated postdecisional regret is a significant contributor to people's decision-making processes, the accuracy of people's regret forecasts has yet to be assessed systematically. Here we report two studies to fill this lacuna. In Study 1, we found that subjects who made reasonably high offers overpredicted the regret that they experienced after they unexpectedly failed at a negotiation. In Study 2, we found that subjects overpredicted the rejoicing and marginally underpredicted the regret that they experienced when they received higher marks than they had expected for their course work. Systematic affective misprediction implies that people making decisions should discount the regret and rejoicing that they anticipate will be associated with potential outcomes arising from those decisions.     

High‐Stakes Decision‐Making Within Complex Social Environments: A Computational Model of Belief Systems in the Arab Spring

People experiencing similar conditions may make different decisions, and their belief systems provide insight about these differences. An example of high‐stakes decision‐making within a complex social context is the Arab Spring, in which large numbers of people decided to protest and even larger numbers decided to stay at home. This study uses qualitative analyses of interview narratives and social media addressing individual decisions to develop a computational model tracing the cognitive decision‐making process. The model builds on work by Abelson and Carroll (1965), Schank and Abelson (1977), and Axelrod (1976) to systematically trace the inferences connecting beliefs to decisions. The findings show that protest decisions were often based on positive emotions such as pride, hope, courage, and solidarity, triggered by beliefs about successful protest and self‐sacrifice. By contrast, decisions to stay at home were triggered by beliefs about safety, state approval, and living conditions. As one participant said, “When I heard about the revolution in Tunisia, my heart was filled with solidarity for the people.” In the words of a non‐participant: “When people are killed, we must be careful. There are more important things than protest: safety and stability.” This study of individual explanations about events identifies the role of emotions in high‐stakes decision‐making within complex social environments.     

Individual differences in causal learning and decision making

In judgment and decision making tasks, people tend to neglect the overall frequency of base-rates when they estimate the probability of an event; this is known as the base-rate fallacy. In causal learning, despite people's accuracy at judging causal strength according to one or other normative model (i.e., Power PC, DeltaP), they tend to misperceive base-rate information (e.g., the cause density effect). The present study investigates the relationship between causal learning and decision making by asking whether people weight base-rate information in the same way when estimating causal strength and when making judgments or inferences about the likelihood of an event. The results suggest that people differ according to the weight they place on base-rate information, but the way individuals do this is consistent across causal and decision making tasks. We interpret the results as reflecting a tendency to differentially weight base-rate information which generalizes to a variety of tasks. Additionally, this study provides evidence that causal learning and decision making share some component processes.     

The detrimental effects of power on confidence, advice taking, and accuracy

Incorporating input from others can enhance decision quality, yet often people do not effectively utilize advice. We propose that greater power increases the propensity to discount advice, and that a key mechanism explaining this effect is elevated confidence in one’s judgment. We investigate the relationships across four studies: a field survey where working professionals rated their own power and confidence and were rated by coworkers on their level of advice taking; an advice taking task where power and confidence were self-reported; and two advice taking experiments where power was manipulated. Results consistently showed a negative relationship between power and advice taking, and evidence of mediation through confidence. The fourth study also revealed that higher power participants were less accurate in their final judgments. Power can thus exacerbate the tendency for people to overweight their own initial judgment, such that the most powerful decision makers can also be the least accurate.     

Excessive confidence in visually-based estimates

People exhibit excessive confidence in visually-based estimates, which in turn biases decision making. Three experiments support this assertion. Experiment 1 shows a strong impact of presentation format on estimation of proportions. Experiment 2 shows that people rely on these erroneous estimates to make incentive-compatible decisions even when objective information can be easily obtained. Experiment 3 demonstrates that the biased decisions disappear when confidence in visually-based estimates is called into question by the perceived complexity of the stimulus.     

Comparing perceptual and preferential decision making

Perceptual and preferential decision making have been studied largely in isolation. Perceptual decisions are considered to be at a non–deliberative cognitive level and have an outside criterion that defines the quality of decisions. Preferential decisions are considered to be at a higher cognitive level and the quality of decisions depend on the decision maker’s subjective goals. Besides these crucial differences, both types of decisions also have in common that uncertain information about the choice situation has to be processed before a decision can be made. The present work aims to acknowledge the commonalities of both types of decision making to lay bare the crucial differences. For this aim we examine perceptual and preferential decisions with a novel choice paradigm that uses the identical stimulus material for both types of decisions. This paradigm allows us to model the decisions and response times of both types of decisions with the same sequential sampling model, the drift diffusion model. The results illustrate that the different incentive structure in both types of tasks changes people’s behavior so that they process information more efficiently and respond more cautiously in the perceptual as compared to the preferential task. These findings set out a perspective for further integration of perceptual and preferential decision making in a single ramework.     

Improving judgement with prepaid expert advice

Decision makers (“Judges”) often make decisions after obtaining advice from an Advisor. The two parties often share a psychological “contract” about what each contributes in expertise to the decision and receives in monetary outcomes from it. In a laboratory experiment, we varied Advisor Experitise and the opportunity for monetary rewards. As expected, these manipulations influenced advice quality, advice taking, and Judge post‐advice decision quality. The main contribution of the study, however, was the manipulation of the timing of monetary rewards (before or after the advising interaction). We found, as predicted, that committing money for expert—but not novice—advice increases Judges' use of advice and their subsequent estimation accuracy. Implications for advice giving and taking are discussed. Copyright © 2004 John Wiley & Sons, Ltd.