Using Effluent Charges in Promoting Investment in Water Pollution Control Technology: A Model of Coordination Failure among Firms

Untreated wastewater being directly discharged into rivers is a very harmful environmental hazard that needs to be tackled urgently in many countries. In order to safeguard the river ecosystem and reduce water pollution, it is important to have an effluent charge policy that promotes the investment of wastewater treatment technology by domestic firms. This paper considers the strategic interaction between the government and the domestic firms regarding the investment in the wastewater treatment technology and the design of optimal e­ffluent charge policy that should be implemented. In this model, the higher is the proportion of non-investing firms, the higher would be the probability of having to incur an e­ffluent charge and the higher would be that charge. On one hand the government needs to impose a sufficiently strict policy to ensure that firms have strong incentive to invest. On the other hand, it cannot be too strict that it drives out firms which cannot afford to invest in such expensive technology. The paper analyses the factors that affect the probability of investment in this technology. It also explains the difficulty of imposing a strict environment policy in countries that have too many small firms which cannot afford to invest unless subsidised.

Expectations Traps and Coordination Failures with Discretionary Policymaking

Discretionary policymakers cannot manage private-sector expectations and cannot coordinate the actions of future policymakers. As a consequence, expectations traps and coordination failures can occur and multiple equilibria can arise. To utilize the explanatory power of models with multiple equilibria it is first necessary to understand how an economy arrives to a particular equilibrium. In this paper we employ notions of learnability and self-enforceability to motivate and identify equilibria of particular interest. Central among these criteria are whether the equilibrium is learnable by private agents and jointly learnable by private agents and the policymaker. We use two New Keynesian policy models to identify the strategic interactions that give rise to multiple equilibria and to illustrate our methods for identifying equilibria of interest. Importantly, unless the Pareto-preferred equilibrium is learnable by private agents, we find little reason to expect coordination on that equilibrium.

Monetary Policy Delegation and Equilibrium Coordination

This paper revisits the argument that the stabilisation bias that arises under discretionary monetary policy can be reduced if policy is delegated to a policymaker with redesigned objectives. We study four delegation schemes: price level targeting, interest rate smoothing, speed limits and straight conservatism. These can all increase social welfare in models with a unique discretionary equilibrium. We investigate how these schemes perform in a model with capital accumulation where uniqueness does not necessarily apply. We discuss how multiplicity arises and demonstrate that no delegation scheme is able to eliminate all potential bad equilibria. Price level targeting has two interesting features. It can create a new equilibrium that is welfare dominated, but it can also alter equilibrium stability properties and make coordination on the best equilibrium more likely.

Coordination in Public Good Provision: How Individual Volunteering is Impacted by the Volunteering of Others

In this analysis, we examine the relationship between an individual's decision to volunteer and the average level of volunteering in the community where the individual resides. Our theoretical model is based on a coordination game , in which volunteering by others is informative regarding the benefit from volunteering. We demonstrate that the interaction between this information and one's private information makes it more likely that he or she will volunteer, given a higher level of contributions by his or her peers. We complement this theoretical work with an empirical analysis using Census 2000 Summary File 3 and Current Population Survey (CPS) 2004-2007 September supplement file data. We control for various individual and community characteristics, and employ robustness checks to verify the results of the baseline analysis. We additionally use an innovative instrumental variables strategy to account for reflection bias and endogeneity caused by selective sorting by individuals into neighborhoods, which allows us to argue for a causal interpretation. The empirical results in the baseline, as well as all robustness analyses, verify the main result of our theoretical model, and we employ a more general structure to further strengthen our results.

Coordination in Public Good Provision: How Individual Volunteering is Impacted by the Volunteering of Others

In this analysis, we examine the relationship between an individual’s decision to volunteer and the average level of volunteering in the community where the individual resides. Our theoretical model is based on a coordination game , in which volunteering by others is informative regarding the benefit from volunteering. We demonstrate that the interaction between this information and one’s private information makes it more likely that he or she will volunteer, given a higher level of contributions by his or her peers. We complement this theoretical work with an empirical analysis using Census 2000 Summary File 3 and Current Population Survey (CPS) 2004-2007 September supplement file data. We control for various individual and community characteristics, and employ robustness checks to verify the results of the baseline analysis. We additionally use an innovative instrumental variables strategy to account for reflection bias and endogeneity caused by selective sorting by individuals into neighbourhoods, which allows us to argue for a causal interpretation. The empirical results in the baseline, as well as all robustness analyses, verify the main result of our theoretical model, and we employ a more general structure to further strengthen our results.

EEG-based functional brain networks: does the network size matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks. Includes self-contained introductions to probability and decision theory. Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models. Features implementation of the methodology with reference to commercial and academically available software. Presents standard networks and their extensions that can be easily implemented and that can assist in the reader's own analysis of real cases. Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning. Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them. Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background. Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.

A cell-based model of extracellular-matrix-guided endothelial cell migration during angiogenesis.

Angiogenesis, the formation of new blood vessels sprouting from existing ones, occurs in several situations like wound healing, tissue remodeling, and near growing tumors. Under hypoxic conditions, tumor cells secrete growth factors, including VEGF. VEGF activates endothelial cells (ECs) in nearby vessels, leading to the migration of ECs out of the vessel and the formation of growing sprouts. A key process in angiogenesis is cellular self-organization, and previous modeling studies have identified mechanisms for producing networks and sprouts. Most theoretical studies of cellular self-organization during angiogenesis have ignored the interactions of ECs with the extra-cellular matrix (ECM), the jelly or hard materials that cells live in. Apart from providing structural support to cells, the ECM may play a key role in the coordination of cellular motility during angiogenesis. For example, by modifying the ECM, ECs can affect the motility of other ECs, long after they have left. Here, we present an explorative study of the cellular self-organization resulting from such ECM-coordinated cell migration. We show that a set of biologically-motivated, cell behavioral rules, including chemotaxis, haptotaxis, haptokinesis, and ECM-guided proliferation suffice for forming sprouts and branching vascular trees.