Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The world is full of uncertainty. In unpredictable circumstances, can emotions facilitate advantageous decision-making? A neuroscience team, led by Antonio Damasio, explored this question using the Iowa Gambling Task (IGT). To the present day, the findings of numerous IGT-related investigations strongly influence clinical and interdisciplinary research, for example, in neuroeconomics and neuromarketing. This special issue examines IGT-based research progress over the past 20 years through literature reviews, clinical examinations, model construction, theoretical integration, and brain imaging technology. Both supportive and opposing viewpoints are provided to frame correlations between rationality, emotion, decision-making, and IGT. Potential future directions for IGT studies are discussed

emotion --- Iowa Gambling Task --- decision-making --- ventromedial prefrontal cortex --- gain-loss frequency --- reward & punishment --- rationality --- expected value --- somatic marker hypothesis

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The world is full of uncertainty. In unpredictable circumstances, can emotions facilitate advantageous decision-making? A neuroscience team, led by Antonio Damasio, explored this question using the Iowa Gambling Task (IGT). To the present day, the findings of numerous IGT-related investigations strongly influence clinical and interdisciplinary research, for example, in neuroeconomics and neuromarketing. This special issue examines IGT-based research progress over the past 20 years through literature reviews, clinical examinations, model construction, theoretical integration, and brain imaging technology. Both supportive and opposing viewpoints are provided to frame correlations between rationality, emotion, decision-making, and IGT. Potential future directions for IGT studies are discussed

emotion --- Iowa Gambling Task --- decision-making --- ventromedial prefrontal cortex --- gain-loss frequency --- reward & punishment --- rationality --- expected value --- somatic marker hypothesis

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Whether managing strategy, operations, or products, making the best decision in a complex, uncertain business environment is challenging. One of the major difficulties facing decision makers is that they often have multiple, competing objectives, which means trade-offs will need to be made. To further complicate matters, uncertainty in the business environment makes it hard to explicitly understand how different objectives will impact potential outcomes. Fortunately, these problems can be solved with a structured framework for multiobjective decision analysis that measures trade-offs among objectives and incorporates uncertainties and risk preferences. This book is designed to help decision makers by providing such an analysis framework implemented as a simple spreadsheet tool. This framework helps structure the decision-making process by identifying what information is needed in order to make the decision, defining how that information should be combined to make the decision, and, finally, providing quantifiable evidence to clearly communicate and justify the final decision. The process itself involves minimal overhead and is perfect for busy professionals who need a simple, structured process for making, tracking, and communicating decisions. With this process, decision making is made more efficient by focusing only on information and factors that are well defined, measureable, and relevant to the decision at hand. The clear characterization of the decision required by the framework ensures that a decision can be traced and is consistent with the intended objectives and organizational values. Using this structured decision-making framework, anyone can effectively and consistently make better decisions to gain a competitive and strategic advantage.

Multiple criteria decision making. --- decision making --- decision analysis --- decision modeling --- strategic decisions --- business decisions --- how to decide --- trade-offs --- multiobjective --- values --- weights --- value functions --- objectives --- measures --- alternatives --- uncertainty --- probability --- discrete --- continuous --- linear --- exponential --- expected value --- utility --- expected utility --- risk tolerance --- certainty equivalents

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

May 2000 - Using the word capital to represent two different concepts is not such a problem when government is responsible for only a small fraction of national investment and is reasonably effective (as in the United States). But when government is a major investor and is ineffective, the gap between capital and cumulative, depreciated investment effort (CUDIE) may be enormous. A public sector steel mill may absorb billions as an investment, but if it cannot produce steel it has zero value as capital. The cost of public investment is not the value of public capital. Unlike for private investors, there is no remotely plausible behavioral model of the government as investor that suggests that every dollar the public sector spends as investment creates capital in an economic sense. This seemingly obvious point has so far been uniformly ignored in the voluminous empirical literature on economic growth, which uses, at best, cumulated, depreciated investment effort (CUDIE) to estimate capital stocks. But in developing countries especially, the difference between investment cumulated at cost and capital value is of primary empirical importance: government investment is half or more of total investment. And perhaps as much as half or more of government investment spending has not created equivalent capital. This suggests that nearly everything empirical written in three broad areas is misguided. First, none of the estimates of the impact of public spending identify the productivity of public capital. Even where public capital could be very productive, regressions and evaluations may suggest that public investment spending has little impact. Second, everything currently said about total factor productivity in developing countries is deeply suspect, as there is no way empirically to distinguish between low output (or growth) attributable to investments that created no factors and low output (or growth) attributable to low (or slow growth in) productivity in using accumulated factors. Third, multivariate growth regressions to date have not, in fact, controlled for the growth of capital stock, so spurious interpretations have emerged. This paper - a product of Poverty and Human Resources, Development Research Group - is part of a larger effort in the group to understand the importance of public sector actions for economic growth.

Accumulation --- Assets --- Capital --- Commodity Prices --- Cost Of Capital --- Debt Markets --- Disclosure --- Economic Growth --- Economic Theory and Research --- Emerging Markets --- Expected Value --- Finance and Financial Sector Development --- Financial Literacy --- Investment --- Investment and Investment Climate --- Investment Flows --- Investment Spending --- Investments --- Labor Policies --- Macroeconomics and Economic Growth --- Non Bank Financial Institutions --- Ownership --- Private Capital --- Private Investors --- Private Sector Development --- Productive Capital --- Profitability --- Public Investment --- Public Sector Economics and Finance --- Share --- Shareholder Value --- Social Protections and Labor --- Value

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

At the heart of human intelligence rests a fundamental puzzle: How are we incredibly smart and stupid at the same time? No existing machine can match the power and flexibility of human perception, language, and reasoning. Yet, we routinely commit errors that reveal the failures of our thought processes. 'What Makes Us Smart' makes sense of this paradox by arguing that our cognitive errors are not haphazard. Rather, they are the inevitable consequences of a brain optimized for efficient inference and decision making within the constraints of time, energy, and memory - in other words, data and resource limitations. Framing human intelligence in terms of these constraints, Samuel Gershman shows how a deeper computational logic underpins the 'stupid' errors of human cognition.

Cognition --- Cognitive psychology. --- Age factors. --- Psychology, Cognitive --- Cognitive science --- Psychology --- Age factors in cognition --- Ability, Influence of age on --- Cognition. --- Intellect. --- Human intelligence --- Intelligence --- Mind --- Ability --- Thought and thinking --- Accuracy and precision. --- Action potential. --- Ad hoc hypothesis. --- Ad hominem. --- Adaptive bias. --- Almost surely. --- Alternative hypothesis. --- Altruism. --- Ambiguity. --- Analogy. --- Anecdote. --- Approximation. --- Attractiveness. --- Bayes' theorem. --- Bayesian inference. --- Bayesian probability. --- Bayesian. --- Behavior. --- Circular reasoning. --- Cognitive flexibility. --- Cognitive style. --- Commitment device. --- Confidence. --- Confirmation bias. --- Conspiracy theory. --- Controllability. --- Counterintuitive. --- Credibility. --- Decision-making. --- Effectiveness. --- Efficacy. --- Efficiency. --- Efficient coding hypothesis. --- Efficient frontier. --- Estimation. --- Expected value. --- Explanation. --- Fair coin. --- Fair market value. --- Gimmick. --- Guessing. --- Heuristic. --- Hot Hand. --- Human intelligence. --- Hypothesis. --- Illusion of control. --- Inductive bias. --- Inference. --- Intelligent design. --- Learnability. --- Lightness (philosophy). --- Likelihood function. --- Logical extreme. --- Logical reasoning. --- Moral hazard. --- Motivated reasoning. --- Mutual exclusivity. --- Natural approach. --- Normative. --- Observation. --- Observational learning. --- Of Miracles. --- Opportunity cost. --- Optimism bias. --- Optimism. --- Our Choice. --- Pairwise comparison. --- Perfect rationality. --- Physical attractiveness. --- Point estimation. --- Politeness. --- Positive feedback. --- Predictability. --- Prediction. --- Predictive coding. --- Predictive power. --- Principle of rationality. --- Prior probability. --- Probability. --- Prosocial behavior. --- Quantity. --- Rational agent. --- Rational choice theory. --- Rationality. --- Reason. --- Reinforcement learning. --- Result. --- Self-control. --- Sophistication. --- Spontaneous recovery. --- Strong inference. --- Suggestion. --- Theory. --- Thought. --- Truth value. --- Uncertainty. --- Utility. --- Value of information. --- With high probability. --- PSYCHOLOGY / Cognitive Psychology & Cognition --- COMPUTERS / Logic Design