Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management

Sea levels are rising in many areas around the world, posing risks to coastal communities and infrastructures. Strategies for managing these flood risks present decision challenges that require a combination of geophysical, economic, and infrastructure models. Previous studies have broken important new ground on the considerable tensions between the costs of upgrading infrastructure and the damages that could result from extreme flood events. However, many risk-based adaptation strategies remain silent on certain potentially important uncertainties, as well as the tradeoffs between competing objectives. Here, we implement and improve on a classic decision-analytical model (Van Dantzig 1956) to: (i) capture tradeoffs across conflicting stakeholder objectives, (ii) demonstrate the consequences of structural uncertainties in the sea-level rise and storm surge models, and (iii) identify the parametric uncertainties that most strongly influence each objective using global sensitivity analysis. We find that the flood adaptation model produces potentially myopic solutions when formulated using traditional mean-centric decision theory. Moving from a single-objective problem formulation to one with multiobjective tradeoffs dramatically expands the decision space, and highlights the need for compromise solutions to address stakeholder preferences. We find deep structural uncertainties that have large effects on the model outcome, with the storm surge parameters accounting for the greatest impacts. Global sensitivity analysis effectively identifies important parameter interactions that local methods overlook, and that could have critical implications for flood adaptation strategies.     

Inventory Policy with Parametric Demand: Operational Statistics,Linear Correction,and Regression

In this paper, we consider data‐driven approaches to the problem of inventory control. We first consider the approach of operational statistics and review related results which enable us to maximize a priori expected profit uniformly over all parameter values, when the demand distribution is known up to the location and scale parameters. For the case of the unknown shape parameter, we first suggest a heuristic approach based on operational statistics to obtain improved ordering policies and illustrate the same for the case of a Pareto demand distribution. In more general cases where the heuristic is not applicable, we suggest linear correction and support vector regression approaches to better estimate ordering policies, and illustrate these using a Gamma demand distribution. In certain cases, our proposed approaches are found to yield significant improvements.     

Growth,inequality and innovation: A CGE analysis of India

This paper probes into the growth and distributional consequences of four basic policy options emanating from the three sources of economic growth, namely, physical capital, human capital and technological progress, with the help of a computable general equilibrium model of India. The simulation results show that, the efficacy of physical capital accumulation in augmenting growth and abating income inequality is greater than that of human capital accumulation. In the long term, however, the latter overtakes the former in promoting growth, but inequality worsens. When the two policies are commingled, growth improves but it continues to be inequality-augmenting. Finally, with concomitant Hicks-neutral technological progress, not only is growth enhanced further, but it turns out to be significantly inequality-mitigating. The emerging policy lesson is that any integrated policy of boosting investments in physical as well as human capital must be closely bound up with technological progress for growth to be inclusive.     

The Efficacy of Neural Networks in Predicting Returns on Stock and Bond Indices*

This paper uses two recently developed tests to identify neglected nonlinearity in the relationship between excess returns on four asset classes and several economic and financial variables. Having found some evidence of possible nonlinearity, it was then investigated whether the predictive power of these variables could be enhanced by using neural network models instead of linear regression or GARCH models. Some evidence of nonlinearity in the relationships between the explanatory variables and large stocks and corporate bonds was found. It was also found that the GARCH models are conditionally efficient with respect to neural network models, but the neural network models outperform GARCH models if financial performance measures are used. In resonance with the results reported for the tests for neglected nonlinearity, it was found that the neural network forecasts are conditionally efficient with respect to linear regression models for large stocks and corporate bonds, whereas the evidence is not statistically significant for small stocks and intermediate-term government bonds. This difference persists even when financial performance measures for individual asset classes are used for comparison.     

Usability of Online Services: The Role of Technology Readiness and Context*

An important prerequisite for the success of any online service is ensuring that customers' experience—via the interface—satisfies both sensory and functional needs. Developing interfaces that are responsive to customers' needs requires a perspective on interface design as well as a deep understanding of the customers themselves. Drawing upon research in consumer behavior concerning consumer beliefs about technology, we deploy an alternative way to describe customers based on psychographic characteristics. Technology readiness (TR), a multidimensional psychographic construct, offers a way to segment online customers based upon underlying positive and negative technology beliefs. The core premise of this study is that the beliefs form the foundation for expectations of how things should work and how specific online service interfaces are evaluated by customers. At the same time, usability evaluations of specific online services might be contingent on contextual factors, specifically the type of site (hedonic vs. utilitarian) and access method (Web vs. wireless Web). The aspects of usability examined here are those incorporated into the usability metric and instrument based on the Microsoft Usability Guidelines (MUG). The results of an empirical study with 160 participants indicate that (i) TR customer segments vary in usability requirements and (ii) usability evaluations of specific online service interfaces are influenced by complex interactions among site type, access method, and TR segment membership. As organizations continue to expand their online service offerings, managers must recognize that the interface exists to serve the customers, so their design must be matched to market needs and TR.     

Dynamic Preference for Flexibility

We consider a decision maker who faces dynamic decision situations that involve intertemporal trade‐offs, as in consumption–savings problems, and who experiences taste shocks that are transient contingent on the state of the world. We axiomatize a recursive representation of choice over state contingent infinite horizon consumption problems, where uncertainty about consumption utilities depends on the observable state and the state follows a subjective Markov process. The parameters of the representation are the subjective process that governs the evolution of beliefs over consumption utilities and the discount factor; they are uniquely identified from behavior. We characterize a natural notion of greater preference for flexibility in terms of a dilation of beliefs. An important special case of our representation is a recursive version of the Anscombe–Aumann model with parameters that include a subjective Markov process over states and state‐dependent utilities, all of which are uniquely identified.     

Investigating Institutional,Economic and Social Determinants of European Regions for Firm Growth Through Employment Generation

This paper examines the influence of the institutional, economic and social characteristics of a region on firm growth through employment generation across 14 European countries for the time period 2010–2013. Theoretically, we utilize the resource‐based view alongside insights from institutional theory to develop a conceptual framework that captures the influence of regional characteristics on firm employment growth. Based on this framework, our empirical results indicate that not only does firm growth depend on the firm‐specific characteristics found in the literature, but regional attributes also significantly impact firm growth in a heterogeneous way for different firm types. In line with the heterogeneous nature of firm growth, our results point to significant differences in the influence of institutional, economic and social characteristics on firm growth in different‐sized groups and across different rates of growth distribution. The implications of our study suggest the importance of managers and policy‐makers realizing which firms are mostly expected to benefit from the external environment, and which in turn can be planned via tailored policy reform by regional governments and firm‐level strategy‐making by managers.