What's a face worth: Noneconomic factors in game playing

Where behavior defies economic analysis, one explanation is that individuals consider more than the immediate payoff. We present evidence that noneconomic factors influence behavior. Attractiveness influences offers in the Ultimatum and Dictator Games. Facial resemblance, a cue of relatedness, increases trusting in a two-node trust game. Only by considering the range of possible influences will game-playing behavior be explained

Emerging Market Business Cycles Revisited: Learning about the Trend

We build an equilibrium business cycle model in which agents cannot perfectly distinguish between the permanent and transitory components of TFP shocks and learn about those components using the Kalman filter. Calibrated to Mexico, the model predicts a higher variability of consumption relative to output and a strongly negative correlation between the trade balance and output for a wide range of variability and persistence of permanent shocks vis-a-vis the transitory shocks. Moreover, our estimation for Mexico and Canada suggests more severe informational frictions in emerging markets than in developed economies.emerging markets, business cycles, learning, Kalman filter

Vaccine Risk Communication: Lessons from Risk Perception, Decision Making and Environmental Risk Communication Research

Dr. Bostrom reviews the rich variety of empirical findings available to guide risk communication and demonstrates how it can contribute to vaccine risk and safety communication

Enhancing the Supply Chain Performance by Integrating Simulated and Physical Agents into Organizational Information Systems

As the business environment gets more complicated, organizations must be able to respond to the business changes and adjust themselves quickly to gain their competitive advantages. This study proposes an integrated agent system, called SPA, which coordinates simulated and physical agents to provide an efficient way for organizations to meet the challenges in managing supply chains. In the integrated framework, physical agents coordinate with inter-organizations\' physical agents to form workable business processes and detect the variations occurring in the outside world, whereas simulated agents model and analyze the what-if scenarios to support physical agents in making decisions. This study uses a supply chain that produces digital still cameras as an example to demonstrate how the SPA works. In this example, individual information systems of the involved companies equip with the SPA and the entire supply chain is modeled as a hierarchical object oriented Petri nets. The SPA here applies the modified AGNES data clustering technique and the moving average approach to help each firm generalize customers\' past demand patterns and forecast their future demands. The amplitude of forecasting errors caused by bullwhip effects is used as a metric to evaluate the degree that the SPA affects the supply chain performance. The experimental results show that the SPA benefits the entire supply chain by reducing the bullwhip effects and forecasting errors in a dynamic environment.Supply Chain Performance Enhancement; Bullwhip Effects; Simulated Agents; Physical Agents; Dynamic Customer Demand Pattern Discovery

The Cowl - v.27 -n.5 - Feb 13, 1974

The Cowl - student newspaper of Providence College. Volume 27, Number 5 - February 13, 1974. 12 pages. Note: The volume number printed on the banner page of this issue (XXVII) duplicates the volume number for 1964-65 academic year

Characterizing adaptive capacity to climate change in developing countries: a case study on Peru

Climate change is expected to impact biodiversity, global temperatures and the water cycle resulting in changes that affect communities in the social system. The capacity to adapt to such climatic fluctuations will vary across social groups, depending on the group\u27s socio-economic and demographic characteristics. This thesis constructs a methodology that bridges existing approaches to study adaptation, providing an alternative that includes high level deterministic approaches to quantify adaptive capacity (AC) and case-by-case strategies to enhance region-specific AC. The proposed methodology consists of an exploratory model and a principal component analysis. The goal of the methodology is to create a sub-national level AC characterization to prioritize domestic policy within a country and to understand the most important factors affecting a region\u27s AC. In addition, the methodology incorporates an important challenge of developing countries: collection, processing, and use of information. To illustrate the methodology, a case study focused on three regions of Peru: Ayacucho, Loreto and Piura. The results of the methodology show that Ayacucho, a region facing low temperatures and glacier retreat, has the lowest AC and thus has priority in the AC strengthening process. The main factors affecting Ayacucho\u27s AC include infrastructure, poverty and low economic capacity. Loreto, with changes in biodiversity and ambient temperatures, has the second highest AC to climate change as a result of a diverse population and low literacy levels. To increase Loreto\u27s AC more transcultural education and family planning might be required. Finally, Piura has the highest AC due to its high economic capacity, but this is affected by the region\u27s social inequality. Minimizing poverty is a critical measure to support and increase AC of this region, which faces increased precipitation, flooding and disease contraction and contagion

Wittgenstein and the Life of Signs

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