a b s t r a c tAccumulation is a fundamental process in dynamic systems: inventory accumulates production less shipments; the national debt accumulates the federal deficit. Effective decision making in such systems requires an understanding of the relationship between stocks and the flows that alter them. However, highly educated people are often unable to infer the behavior of simple stock-flow systems. In a series of experiments we demonstrate that poor understanding of accumulation, termed stock-flow failure, is a fundamental reasoning error. Persistent poor performance is not attributable to an inability to interpret graphs, lack of contextual knowledge, motivation, or cognitive capacity. Rather, stock-flow failure is a robust phenomenon that appears to be rooted in failure to appreciate the most basic principles of accumulation, leading to the use of inappropriate heuristics. We show that many people, including highly educated individuals with strong technical training, use what we term the ''correlation heuristic", erroneously assuming that the behavior of a stock matches the pattern of its flows. We discuss the origins of stock-flow failure and implications for management and education.Ó 2008 Elsevier Inc. All rights reserved.Understanding and managing stocks and flows-that is, resources that accumulate or deplete and the flows that alter them-is a fundamental process in society, business, and personal life. At the macroeconomic level, for example, exploration increases known petroleum reserves, while oil production reduces the stock of oil remaining for the future. In turn, petroleum combustion increases the stock of carbon dioxide in the atmosphere and contributes to global warming. At the organizational level, firms' capabilities and competitive advantages arise from the accumulation of resources and knowledge. Firms must manage their cash flows to maintain adequate stocks of working capital, and production must be adjusted as sales vary to maintain sufficient inventory. Individuals, too, face similar stock management challenges: we manage our bank accounts (stock of funds) to maintain a reasonable balance as our incomes (inflows) and expenses (outflows) vary, and we struggle to maintain a healthy weight by managing the inflow and outflow of calories through diet and exercise. Accumulation is a pervasive process in everyday life, and arises at every temporal, spatial and organizational scale.All stock-flow systems share the same underlying structure. The resource level (stock) accumulates the inflows to it less the outflows from it. 1 Although the relationship between stocks and flows is a fundamental concept of calculus, knowledge of calculus is not necessary to understand the behavior of stocks and flows. Any stock can be thought of as the amount of water in a tub. The water level accumulates the flow of water into the tub (the inflow)0749-5978/$ -see front matter Ó 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.obhdp.2008.03.003 * Corresponding author. E-mail addresses: mcronin@gmu.edu (M.A. ...

This paper presents a learning theory pertinent to dynamic decision making (DDM) called instancebased learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision-making process: instance-based knowledge, recognition-based retrieval, adaptive strategies, necessity-based choice, and feedback updates. IBLT suggests in DDM people learn with the accumulation and refinement of instances, containing the decision-making situation, action, and utility of decisions. As decision makers interact with a dynamic task, they recognize a situation according to its similarity to past instances, adapt their judgment strategies from heuristic-based to instance-based, and refine the accumulated knowledge according to feedback on the result of their actions. The IBLT's learning mechanisms have been implemented in an ACT-R cognitive model. Through a series of experiments, this paper shows how the IBLT's learning mechanisms closely approximate the relative trend magnitude and performance of human data. Although the cognitive model is bounded within the context of a dynamic task, the IBLT is a general theory of decision making applicable to other dynamic environments.

Many decisions in the lives of animals and humans require a fine balance between the exploration of different options and the exploitation of their rewards. Do you buy the advertised car, or do you testdrive different models? Do you continue feeding from the current patch of flowers, or do you fly off to another one? Do you marry your current partner, or try your luck with someone else? The balance required in these situations is commonly referred to as the exploration-exploitation tradeoff. It features prominently in a wide range of research traditions, including learning, foraging, and decisionmaking literatures. Here, we integrate findings from these and other often-isolated literatures in order to gain a better understanding of the possible tradeoffs between exploration and exploitation, and we propose new theoretical insights that might guide future research. Specifically, we explore how potential tradeoffs depend on (1) the conceptualization of exploration and exploitation; (2) the influencing environmental, social, and individual factors; (3) the scale at which exploration and exploitation are considered; (4) the relationship and types of transitions between the two behaviors; and (5) the goals of the decision maker. We conclude that exploration and exploitation are best conceptualized as points on a continuum, and that the extent to which an agent's behavior can be interpreted as exploratory or exploitative depends upon the level of abstraction at which it is considered.

In decisions from experience, there are 2 experimental paradigms: sampling and repeated-choice. In the sampling paradigm, participants sample between 2 options as many times as they want (i.e., the stopping point is variable), observe the outcome with no real consequences each time, and finally select 1 of the 2 options that cause them to earn or lose money. In the repeated-choice paradigm, participants select 1 of the 2 options for a fixed number of times and receive immediate outcome feedback that affects their earnings. These 2 experimental paradigms have been studied independently, and different cognitive processes have often been assumed to take place in each, as represented in widely diverse computational models. We demonstrate that behavior in these 2 paradigms relies upon common cognitive processes proposed by the instance-based learning theory (IBLT; Gonzalez, Lerch, & Lebiere, 2003) and that the stopping point is the only difference between the 2 paradigms. A single cognitive model based on IBLT (with an added stopping point rule in the sampling paradigm) captures human choices and predicts the sequence of choice selections across both paradigms. We integrate the paradigms through quantitative model comparison, where IBLT outperforms the best models created for each paradigm separately. We discuss the implications for the psychology of decision making.

A common practice in cognitive modeling is to develop new models specific to each particular task. We question this approach and draw on an existing theory, instance-based learning theory (IBLT), to explain learning behavior in three different choice tasks. The same instance-based learning model generalizes accurately to choices in a repeated binary choice task, in a probability learning task, and in a repeated binary choice task within a changing environment. We assert that, although the three tasks are different, the source of learning is equivalent and therefore, the cognitive process elicited should be captured by one single model. This evidence supports previous findings that instance-based learning is a robust learning process that is triggered in a wide range of tasks from the simple repeated choice tasks to the most dynamic decision making tasks.

Dynamic decision-making (DDM) research grew out of a perceived need for understanding how people control dynamic, complex, real-world systems. DDM has describable characteristics and, with some unavoidable sacrifice of realism, is suitable for study in a laboratory setting through the use of complex computer simulations commonly called 'microworlds'. This paper presents a taxonomic definition of DDM, an updated review of existing microworlds and their characteristics, and a set of cognitive demands imposed by DDM tasks. Although the study of DDM has garnered little attention to date, we believe that both technological advancement and the relationships between DDM and naturalistic decision making, complex problem solving, and general systems theory have made DDM a viable process by which to study how people make decisions in dynamic, real-world environments.

The ''framing effect'' is observed when the description of options in terms of gains (positive frame) rather than losses (negative frame) elicits systematically different choices. Few theories explain the framing effect by using cognitive information-processing principles. In this paper we present an explanatory theory based on the cost-benefit tradeoffs described in contingent behavior. This theory proposes that individuals examining various alternatives try to determine how to make a good decision while expending minimal cognitive effort. For this study, we used brain activation functional magnetic resonance imaging (fMRI) to evaluate individuals that we asked to choose between one certain alternative and one risky alternative in response to problems framed as gains or losses. Our results indicate that the cognitive effort required to select a sure gain was considerably lower than the cognitive effort required to choose a risky gain. Conversely, the cognitive effort expended in choosing a sure loss was equal to the cognitive effort expended in choosing a risky loss. fMRI revealed that the cognitive functions used by the decision makers in this study were localized in the prefrontal and parietal cortices of the brain, a finding that suggests the involvement of working memory and imagery in the selection process.

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