Overreaction to Fearsome Risks

When risks threaten, cognitive mechanisms bias people toward action or inaction. Fearsome risks are highly available. The availability bias tells us that this leads people to overestimate their frequency. Therefore, they also overreact to curtail the likelihood or consequences of such risks. More generally, fear can paralyze efforts to think clearly about risks. We draw on a range of environmental risks to show the following: (1) Fear leads us to neglect probability of occurrence; (2) As fearsome environmental risks are usually imposed by others (as externalities), indignation stirs excess reaction; (3) We often misperceive or miscalculate such risks. Two experiments demonstrate probability neglect when fearsome risks arise: (a) willingness-to-pay to eliminate the cancer risk from arsenic in water (described in vivid terms) did not vary despite a 10-fold variation in risk; (b) the willingness-to-accept price for a painful but non dangerous electric shock did not vary between a 1 and 100% chance. Possible explanations relate to the role of the amygdala in impairing cognitive brain function. Government and the law, both made by mortals and both responding to public pressures, similarly neglect probabilities for fearsome risks. Examples relating to shark attacks, Love Canal, alar and terrorism are discussed.     

The inflation and inflation uncertainty relationship for Turkey: a dynamic framework

This article assesses the interaction between inflation and inflation uncertainty in a dynamic framework for Turkey by using monthly data for the time period 1984–2009. The bulk of previous studies investigating the link between inflation and inflation uncertainty employ Autoregressive Conditional Heteroskedasticity (ARCH)-type models, which consider inflation uncertainty as a predetermined function of innovations to inflation specification. The stochastic volatility in mean (SVM) models that we use allow for gathering innovations to inflation uncertainty and assess the effect of inflation volatility shocks on inflation over time. When we assess the interaction between inflation and its volatility, the empirical findings indicate that response of inflation to inflation volatility is positive and statistically significant. However, the response of inflation volatility to inflation is negative but not statistically significant.     

Minsky Goes to Buffalo—and Takes on the Economics Establishment

This essay is based on remarks presented by the author at The Fourth Bi-Annual Cross-Border Post Keynesian Conference, Buffalo State College, on October 9, 2009. It addresses the economic challenges facing Buffalo, New York, and countless other American cities, especially in the Northeast and Midwest; draws on the writings of Hyman Minsky to offer an interpretation of what many now call the Great Recession, which began in late 2007; and challenges the image of Minsky presented by mainstream economists and journalists, with special attention to a recent lecture by Paul Krugman. The essay closes by returning to Buffalo, where—as Minsky anticipated in the 1990s—the economic fate of working families depends largely on the outcome of a national struggle over the shape of future U.S. economic transformation.     

Does a factor Phillips curve help? An evaluation of the predictive power for U.S. inflation

This article evaluates various models’ predictive power for U.S. inflation rate using a simulated out-of-sample forecasting framework. The starting point is the traditional unemployment Phillips curve. We show that a factor Phillips curve model is superior to the traditional Phillips curve, and its performance is comparable to other factor models. We find that a factor AR model is superior to the factor Phillips curve model, and is the best bivariate or factor model at longer horizons. Finally, we investigate a New Keynesian Phillips curve model, and find that its forecasting performance dominates all other models at the longer horizons.     

Amending China’s Notion of a “Consumer”: Lessons from Comparative Analysis of the PRC Consumer Protection Law

Who does the law treat as a “consumer” and why does it matter? How should China’s notion of a “consumer” best be articulated within the law and applied in practice? This article will attempt to answer these intriguing questions by first focusing on the approach taken to define a “consumer” in China’s Law on the Protection of Consumer Rights and Interests before examining the legal notion of a “consumer” in comparative perspective, in order to further understand the competing rationales behind the consumer protection law. This article will explore this Chinese definition of a “consumer” to propose how China’s vague and unworkable statutory definition of a ‘consumer’ should be amended in future.

     

The Inhibitory Effect of Political Conservatism on Consumption: The Case of Fair Trade

Journal of Business Ethics - By synthesizing the argumentation theory of new rhetoric with research on heuristics and motivated reasoning, we develop a conceptual view of argumentation based on...     

Search reduction in hierarchical distributed problem solving

Knoblock and Korf have determined that abstraction can reduce search at a single agent from exponential to linear complexity (Knoblock 1991; Korf 1987). We extend their results by showing how concurrent problem solving among multiple agents using abstraction can further reduce search to logarithmic complexity. We empirically validate our formal analysis by showing that it correctly predicts performance for the Towers of Hanoi problem (which meets all of the assumptions of the analysis). Furthermore, a powerful form of abstraction for large multiagent systems is to group agents into teams, and teams of agents into larger teams, to form an organizational pyramid. We apply our analysis to such an organization of agents and demonstrate the results in a delivery task domain. Our predictions about abstraction's benefits can also be met in this more realistic domain, even though assumptions made in our analysis are violated. Our analytical results thus hold the promise for explaining in general terms many experimental observations made in specific distributed AI systems, and we demonstrate this ability with examples from prior research.This research has been sponsored, in part, by the National Science Foundation under grants IRI-9015423 and IRI-9010645, by the University of Michigan Rackham Graduate School, and by a Bell Northern Research Postgraduate Award.     

The Staff Ride: Developing Historical Sense in Army Leaders

In the current essay, the author draws on his experience as a serving member of the Armed Forces to discuss the problem of developing military leaders capable of mitigating the disruptive nature of war. Specifically, it discusses the staff ride, a comprehensive analysis of historical events conducted through observation, reflection, and discussion, to develop in leaders an understanding that nothing ever works out the way a leader intends or expects. For the military leader facing the uncertainty and ambiguity of modern combat, this lesson from history is arguably the most essential.     

Premium setting and bank behavior in a voluntary deposit insurance scheme

This study analyzes the effect of premium rates on banks’ incentives to join a deposit insurance scheme and their incentives to invest in risky projects under a voluntary deposit insurance scheme. We find that in order to maximize social welfare, the insurance agency must either set the premium rate to be low so as to attract all banks to join the insurance scheme, or not to have the deposit insurance at all. However, the low premium rate in the voluntary scheme does not balance the budget of the deposit insurance. We also show that in the compulsory deposit insurance scheme, however, it is possible to impose an optimal premium rate that can balance the insurance agency’s budget and achieve the highest social welfare. The results also present the dominance of the compulsory scheme over the voluntary scheme in terms of maximizing social welfare and balancing the budget.