Learning From Small Samples: An Analysis of Simple Decision Heuristics

Abstract Simple decision heuristics are models of human and animal behavior that use few pieces of information-perhaps only a single piece of information-and integrate the pieces in simple ways, for example, by considering them sequentially, one at a time, or by giving them equal weight. We focus on three families of heuristics: single-cue decision making, lexicographic decision making, and tallying. It is unknown how quickly these heuristics can be learned from experience. We show, analytically and empirically, that substantial progress in learning can be made with just a few training samples. When training samples are very few, tallying performs substantially better than the alternative methods tested. Our empirical analysis is the most extensive to date, employing 63 natural data sets on diverse subjects

Analogical Transfer in Multi-Attribute Decision Making

People often must make inferences in domains with limited information. In such cases, they can leverage their knowledge from other domains to make these inferences. This knowledge transfer process is quite common, but what are the underlying mechanisms that allow us to accomplish it? Analogical reasoning may be one such mechanism. This dissertation explores the role of analogy in influencing decision-making performance when faced with a new domain. We delve into the knowledge transferred between tasks and how this influences decision-making in novel tasks. Experiment I has two conditions, and each condition has two tasks. In one condition, the two task domains are analogically related, where for example, participants make inferences first about water flow and then about heat flow. In the second condition, the domains do not share obvious similarities. For example, car efficiency and water flow. Experiment I shows that participants presented with an analogy demonstrated better performance than those without. We hypothesize that this knowledge transfer occurs in two ways: firstly, analogical mapping enhances comprehension of cue utilization in a new task; secondly, the strategy employed is transferred. In Chapter 3, we developed a machine learning technique to uncover the strategies used by participants. Our findings reveal that the best-performing strategy from the old task is typically carried over to the new task. In Chapter 4, we developed a model of analogical transfer in multi-attribute decision making. We use the ACT-R theory of cognition as a framework to model knowledge transfer by integrating a reinforcement learning model of strategy selection with a model of analogy. The simulation results showcase a similar trend of both accuracy and strategy use to the behavioral data. Finally, we critically analyze our study\u27s limitations and outline promising directions for future research, thereby paving the way for a deeper understanding of knowledge transfer mechanisms

Modeling the adaptation of search termination in human decision making.

We study how people terminate their search for information when making decisions in a changing environment. In 3 experiments, differing in the cost of search, participants made a sequence of 2-alternative decisions, based on the information provided by binary cues they could search. Whether limited or extensive search was required to maintain accurate decisions changed across the course of the experiment, but was not indicated to participants. We find large individual differences but that, in general, the extent of search is changed in response to environmental change, and is not necessarily triggered by a reduction in accuracy. We then examine the ability of 4 models to account for individual participant behavior, using a generalization measure that tests model predictions. Two of the models use reinforcement learning, and differ in whether they use error or both error and effort signals to control how many cues are searched. The other 2 models use sequential sampling processes, and differ in the regulatory mechanisms they use to adjust the decision thresholds that control the extent of search. We find that error-based reinforcement learning is usually an inadequate account of behavior, especially when search is costly. We also find evidence in the model predictions for the use of confidence as a regulatory variable. This provides an alternative theoretical approach to balancing error and effort, and highlights the possibility of hierarchical regulatory mechanisms that lead to delayed and abrupt changes in the extent of search

Decision making study: methods and applications of evidential reasoning and judgment analysis

Decision making study has been the multi-disciplinary research involving operations researchers, management scientists, statisticians, mathematical psychologists and economists as well as others. This study aims to investigate the theory and methodology of decision making research and apply them to different contexts in real cases. The study has reviewed the literature of Multiple Criteria Decision Making (MCDM), Evidential Reasoning (ER) approach, Naturalistic Decision Making (NDM) movement, Social Judgment Theory (SJT), and Adaptive Toolbox (AT) program. On the basis of these literatures, two methods, Evidence-based Trade-Off (EBTO) and Judgment Analysis with Heuristic Modelling (JA-HM), have been proposed and developed to accomplish decision making problems under different conditions. In the EBTO method, we propose a novel framework to aid people s decision making under uncertainty and imprecise goal. Under the framework, the imprecise goal is objectively modelled through an analytical structure, and is independent of the task requirement; the task requirement is specified by the trade-off strategy among criteria of the analytical structure through an importance weighting process, and is subject to the requirement change of a particular decision making task; the evidence available, that could contribute to the evaluation of general performance of the decision alternatives, are formulated with belief structures which are capable of capturing various format of uncertainties that arise from the absence of data, incomplete information and subjective judgments. The EBTO method was further applied in a case study of Soldier system decision making. The application has demonstrated that EBTO, as a tool, is able to provide a holistic analysis regarding the requirements of Soldier missions, the physical conditions of Soldiers, and the capability of their equipment and weapon systems, which is critical in domain. By drawing the cross-disciplinary literature from NDM and AT, the JA-HM extended the traditional Judgment Analysis (JA) method, through a number of novel methodological procedures, to account for the unique features of decision making tasks under extreme time pressure and dynamic shifting situations. These novel methodological procedures include, the notion of decision point to deconstruct the dynamic shifting situations in a way that decision problem could be identified and formulated; the classification of routine and non-routine problems, and associated data alignment process to enable meaningful decision data analysis across different decision makers (DMs); the notion of composite cue to account for the DMs iterative process of information perception and comprehension in dynamic task environment; the application of computational models of heuristics to account for the time constraints and process dynamics of DMs decision making process; and the application of cross-validation process to enable the methodological principle of competitive testing of decision models. The JA-HM was further applied in a case study of fire emergency decision making. The application has been the first behavioural test of the validity of the computational models of heuristics, in predicting the DMs decision making during fire emergency response. It has also been the first behavioural test of the validity of the non-compensatory heuristics in predicting the DMs decisions on ranking task. The findings identified extend the literature of AT and NDM, and have implications for the fire emergency decision making

Heuristics for ordering cue search in decision making

Simple lexicographic decision heuristics that consider cues one at a time in a particular order and stop searching for cues as soon as a decision can be made have been shown to be both accurate and frugal in their use of information. But much of the simplicity and success of these heuristics comes from using an appropriate cue order. For instance, the Take The Best heuristic uses validity order for cues, which requires considerable computation, potentially undermining the computational advantages of the simple decision mechanism. But many cue orders can achieve good decision performance, and studies of sequential search for data records have proposed a number of simple ordering rules that may be of use in constructing appropriate decision cue orders as well. Here we consider a range of simple cue ordering mechanisms, including tallying, swapping, and move-to-front rules, and show that they ca

Validierung des Desired Level of Confidence

In der vorliegenden Arbeit wird in acht Studien das Desired Level of Confidence (DLC) von Hausmann und Läge (2008) validiert. Das DLC ist ein individuelles Maß des Abbruchs der Informationssuche im Entscheidungsprozess: Erst wenn die Validität einer Information über dem individuellen Anspruchsniveau an gewünschter Urteilssicherheit (Hausmann- Thürig, 2004) liegt, wird die Suche nach weiteren Informationen abgebrochen und eine Entscheidung getroffen. Das DLC kann in einer Entscheidungsaufgabe - der Pferdewette - gemessen werden. In Studie 1 wurde untersucht, ob das DLC in der Messaufgabe reliabel erfasst werden kann. Dazu wurde die Vorhersagekraft des DLC für das Entscheidungsverhalten in einem unabhängingen Set von Aufgaben bestimmt. Weiterhin wurde untersucht, inwieweit das DLC von irrelevanten Merkmalen der Messaufgabe abhängig ist. Es zeigt sich, dass das DLC Modell Entscheidungsverhalten gut vorhersagen kann und dass eine Veränderung der Merkmale der Messaufgabe keinen Einfluss auf die interindividuellen Unterschiede im DLC hat; die Korrelation zwischen dem DLC der Pferdewette und dem DLC der für die Studie entwickelten Messaufgabe - dem Kriminalfall - liegt bei einem Pearson’s r = .79. In Studie 2 wurde untersucht, wie zeitlich stabil das DLC ist und inwieweit es sich damit beim DLC um ein Persönlichkeitsmerkmal handelt. Weiterhin wurde getestet, wie das DLC mit Prozessmaßen einer Entscheidung und mit der Art der Integration von Informationen zusammenhängt. Es zeigt sich, dass die Messungen des DLC über einen Zeitraum von einer Woche mit einem Pearson’s r = .61 zeitlich stabil ist, dass Probanden mit einem höheren DLC tendenziell mehr Information in einer Entscheidungsaufgabe - dem Börsenspiel - aufdecken (Pearson’s r = .42) und dass das DLC nicht mit der Art der Integration der Informationen zusammenhängt. In den folgenden vier Studien wurde untersucht, ob das DLC mit situationalen Bedingungen interagiert. In Studie 3 und 4 wurde untersucht, ob ein Priming eines One-Reason Entscheidungsverhaltens (ORDM) - das ist ein Entscheidungsverhalten, in dem wenige Informationen berücksichtigt werden - vs. More-Reason Entscheidungsverhalten (MRDM) - das ist ein Entscheidungsverhalten, in dem viele Informationen berücksichtigt werden - zu einem niedrigen vs. hohen DLC führt. Es zeigt sich, dass weder ein konzeptuelles Priming noch ein Mindset Priming einen Einfluss auf die Höhe des DLC hat. In Studie 5 und 6 wurde untersucht, ob Probanden, die ein hohes vs. niedriges situationales Need for Cognitive Closure (Kruglanski & Webster, 1996) induziert bekommen, ein geringes vs. hohes DLC haben. Es zeigt sich, dass weder die Attraktivität der Folgeaufgabe noch ein aversiver Ton in der Erhebungssituation auf die Höhe des DLC wirken. In den abschließenden Studien wurde untersucht, ob das DLC mit Persönlichkeitsvariablen zusammenhängt. In Studie 7 und 8 wurde untersucht, ob das DLC mit Antworttendenzen in einer Signalentdeckungsaufgabe zusammenhängt. Es zeigt sich, dass Probanden mit einem hohen DLC weder in einer Kategorisierungsaufgabe noch in einer Rekognitionsaufgabe ein signifikant konservativeres Entscheidungskriterium wählen. Es zeigt sich über die Studien hinweg kein stabiler signifikanter Zusammenhang zwischen dem DLC und dem Bedürfnis nach kognitiver Geschlossenheit (16-NCCS) (Schlink &Walther, 2007), dem regulatorischen Fokus (Werth & Förster, 2007), der Achievement Motives Scale (AMS-R) (Lang & Fries, 2006) und - bis auf einen signifikant positiven Zusammenhang mit der Offenheit für Erfahrung (Pearson’s r = .27) - mit den Big Five der Persönlichkeit (Borkenau & Ostendorf, 1993). Statistische Probleme, wie die geringe Teststärke in den Studien, konzeptuelle Probleme und methodische Probleme, wie die verwendete Messmethode zur Bestimmung des DLC, werden abschließend diskutiert

Individual differences & instance based decision making: putting “bounded rationality” to the test

Instance based risk taking behaviour allows relatively little time for information processing and may be responsible for unconsciously driven erratic behaviour in judgment and decision making. Previous theories that have explored the factors involved in risk taking behaviour include dispositional, decision-making, and neurocognitive functioning based theories. The present studies examine the contributions of age and gender, emotional intelligence, dispositional traits and affective states, involved in instance based decision-making. Participants were assessed using a binary choice task (study 1-A) and a double gamble risk taking task (study 1-B) which involved choices between financial gains with different pay-offs and risk levels, and an ignorance based task (study 2) which involved ignorance based judgments in the classic city size task. Participants were also administered the Trait Emotional Intelligence questionnaire, the Positive Affect Negative Affect Schedule, a self-report measure based on Gray’s behavioural activation and behavioural inhibition systems theory (BIS/BAS scale), and Dickman’s Impulsivity Inventory which distinguishes in functional and dysfunctional impulsivity. The purpose of these studies was to investigate whether individual differences in personality, emotional intelligence (EI) and affect predicted instance based risk taking behaviour. The participants were 64 (study 1-A), 68 (study 1-B), and 73 (study 2) university students; In study 1-A there were significant correlations between positive affect (PA), BAS Drive, BAS Fun-Seeking (FS), and total BAS and the number of risky choices in the binary choice task (r = .28, .25, .26, .31; p= .02, .04, .04, .01). In study 1-B there were significant correlations between PA, FS and total BAS and the number of risky choices in the binary choice double gamble task (r = .24, .25, .32; p = .04, .04, .01). There were no significant associations of trait EI or (functional or dysfunctional) impulsivity with the number of risky choices. These results indicate that individuals who are high in PA, BAS Drive and BAS Fun-Seeking tend to be riskier in decision making involving monetary incentives on an instance based decision making task. In study 2 there were significant correlations between functional impulsivity and negative affect and absolute scores of the Discrimination Index (r = .26, 33; p = .02, .01). These results indicate that individuals who are high in FI and NA tend to base their judgments and decision making on recognition heuristic use. The findings of the three studies indicate that dispositional variables and affective states may play a very important role in instance based risk taking behaviour. Also they indicate that affect is an important factor in instance based decision making, but the role of impulsivity is less clear. The findings in general imply a connection between personality and affective states and performance in professional risk laden domains such as the security, finances, and insurance sectors where individuals are called to take split second decisions