Intelligent process development of foam molding for the Thermal Protection System (TPS) of the space shuttle external tank

A knowledge based system to assist process engineers in evaluating the processability and moldability of poly-isocyanurate (PIR) formulations for the thermal protection system of the Space Shuttle external tank (ET) is discussed. The Reaction Injection Molding- Process Development Advisor (RIM-PDA) is a coupled system which takes advantage of both symbolic and numeric processing techniques. This system will aid the process engineer in identifying a startup set of mold schedules and in refining the mold schedules to remedy specific process problems diagnosed by the system

Learning why things change: The Difference-Based Causality Learner

In this paper, we present the Difference-Based Causality Learner (DBCL), an algorithm for learning a class of discrete-time dynamic models that represents all causation across time by means of difference equations driving change in a system. We motivate this representation with real-world mechanical systems and prove DBCL's correctness for learning structure from time series data, an endeavour that is complicated by the existence of latent derivatives that have to be detected. We also prove that, under common assumptions for causal discovery, DBCL will identify the presence or absence of feedback loops, making the model more useful for predicting the effects of manipulating variables when the system is in equilibrium. We argue analytically and show empirically the advantages of DBCL over vector autoregression (VAR) and Granger causality models as well as modified forms of Bayesian and constraintbased structure discovery algorithms. Finally, we show that our algorithm can discover causal directions of alpha rhythms in human brains from EEG data

Empirical Validation of Agent Based Models: A Critical Survey

This paper addresses the problem of finding the appropriate method for conducting empirical validation in agent-based (AB) models, which is often regarded as the Achilles’ heel of the AB approach to economic modelling. The paper has two objectives. First, to identify key issues facing AB economists engaged in empirical validation. Second, to critically appraise the extent to which alternative approaches deal with these issues. We identify a first set of issues that are common to both AB and neoclassical modellers and a second set of issues which are specific to AB modellers. This second set of issues is captured in a novel taxonomy, which takes into consideration the nature of the object under study, the goal of the analysis, the nature of the modelling assumptions, and the methodology of the analysis. Having identified the nature and causes of heterogeneity in empirical validation, we examine three important approaches to validation that have been developed in AB economics: indirect calibration, the Werker-Brenner approach, and the history-friendly approach. We also discuss a set of open questions within empirical validation. These include the trade-off between empirical support and tractability of findings, the issue of over-parameterisation, unconditional objects, counterfactuals, and the non-neutrality of data.Empirical validation, agent-based models, calibration, history-friendly modelling

Theory-driven learning : using intra-example relationships to constrain learning

We describe an incremental learning algorithm, called theory-driven learning, that creates rules to predict the effect of actions. Theory-driven learning exploits knowledge of regularities among rules to constrain the learning problem. We demonstrate that this knowledge enables the learning system to rapidly converge on accurate predictive rules and to tolerate more complex training data. An algorithm for incrementally learning these regularities is described and we provide evidence that the resulting regularities are sufficiently general to facilitate learning in new domains

Unpacking constructs: a network approach for studying war exposure, daily stressors and post-traumatic stress disorder

Conflict affected populations are exposed to stressful events during and after war, and it is well established that both take a substantial toll on individuals' mental health. Exactly how exposure to events during and after war affect mental health is a topic of considerable debate. Various hypotheses have been put forward on the relation between stressful war exposure (SWE), daily stressors (DS) and the development of post-traumatic stress disorder (PTSD). This paper seeks to contribute to this debate by critically reflecting upon conventional modeling approaches and by advancing an alternative model to studying interrelationships between SWE, DS, and PTSD variables. The network model is proposed as an innovative and comprehensive modeling approach in the field of mental health in the context of war. It involves a conceptualization and representation of variables and relationships that better approach reality, hence improving methodological rigor. It also promises utility in programming and delivering mental health support for war-affected populations

The New Keynesian Phillips Curve and Inflation Expectations: Re-Specification and Interpretation

A theoretical analysis of the new Keynesian Phillips curve (NKPC) is provided, formulating the conditions under which the NKPC coincides with a real-world relation that is not spurious or misspecified. A time-varying-coefficient (TVC) model, involving only observed variables, is shown to exactly represent the underlying “true” NKPC under certain conditions. In contrast, “hybrid” NKPC models, which add lagged-inflation and supply-shock variables, are shown to be spurious and misspecified. We also show how to empirically implement the NKPC under the assumption that expectations are formed rationally.Time-varying-coefficient model; Inflation-unemployment trade-off; “Objective” probability; Spurious correlation; Rational expectation; Coefficient driver

Institutional complementarities and gender diversity on boards: a configurational approach

Manuscript Type: Empirical Research Question/Issue: To address the lack of a complementarities-based approach in studies of board diversity, this paper seeks to understandwhether and howcertain country-level factors are causally and jointly related towomen on boards and the nature of their complementarities (are they synergic or substitutes?). Moreover, we intend to learn more about the adoption/diffusion of board gender quotas, by taking into account their role in the existing national configurations (whether they are necessary and/or sufficient conditions). Research Findings/Insights: Using fs/QCA, our findings reveal a particular configuration of country-level conditions that supports the existence of a joint causal relation between given institutional arrangements. Furthermore, we find that board gender quota legislation is not a sufficient condition on its own to achieve a higher number of women on boards. Such evidence suggests that its diffusion across countries could be the result of institutional isomorphismor social legitimacy more than to rational reasons. Theoretical/Academic Implications: For scholars, our paper refines and expands insights from the extant comparative corporate governance literature. By finding support for the “bundled” or jointly causal nature of given institutional factors,we open a window for further research that investigates board-level phenomena in a complementarities-based perspective. Practitioner/Policy Implications: For policymakers, this study provides some insights that could better drive their choice about which mix of policies is necessary to improve female representation on boards, and especially in which institutional areas they should be implemented. It is particularly relevant, because once gender quotas are endorsed at board level, they could have ambiguous effects on firm performance and corporate governance

Political Accountability, Fiscal Conditions, and Local Government Performance – Cross-Sectional Evidence from Indonesia

What makes governments tick? Why are some public institutions more successful than others in managing resources and delivering services? And even more vitally, how can malfunctioning institutions be reformed so that they perform their responsibilities more effectively? This paper contributes to our understanding of theses overarching questions by exploring the interactions between political institutions and public sector performance in the context of decentralization and local governance. It shows -both theoretically and empirically- that performance outcomes are determined by the extent to which people can hold their governments accountable through political institutions. The basic hypothesis underlying this research is that political accountability, either by encouraging sanctions upon non-compliant public agents or simply by reducing the informational gap regarding government activities, will create forceful incentives for elected officials and civil servants to reduce opportunistic behavior and improve performance. Using a cross-sectional regression the hypothesis is empirically tested against evidence from newly empowered local governments in Indonesia. The empirical findings broadly support our hypotheses. Improved public services on the ground, both in terms of quantity and quality, require informed and well functioning decision making processes that allocate resources to priority areas that meet the demand of the broader community.governance, public services, fiscal decentralization

Behavior change interventions: the potential of ontologies for advancing science and practice

A central goal of behavioral medicine is the creation of evidence-based interventions for promoting behavior change. Scientific knowledge about behavior change could be more effectively accumulated using "ontologies." In information science, an ontology is a systematic method for articulating a "controlled vocabulary" of agreed-upon terms and their inter-relationships. It involves three core elements: (1) a controlled vocabulary specifying and defining existing classes; (2) specification of the inter-relationships between classes; and (3) codification in a computer-readable format to enable knowledge generation, organization, reuse, integration, and analysis. This paper introduces ontologies, provides a review of current efforts to create ontologies related to behavior change interventions and suggests future work. This paper was written by behavioral medicine and information science experts and was developed in partnership between the Society of Behavioral Medicine's Technology Special Interest Group (SIG) and the Theories and Techniques of Behavior Change Interventions SIG. In recent years significant progress has been made in the foundational work needed to develop ontologies of behavior change. Ontologies of behavior change could facilitate a transformation of behavioral science from a field in which data from different experiments are siloed into one in which data across experiments could be compared and/or integrated. This could facilitate new approaches to hypothesis generation and knowledge discovery in behavioral science

SAMUELSON'S FULL DUALITY AND THE USE OF DIRECTED ACYCLICAL GRAPHS: THE BIRTH OF CAUSALLY IDENTIFIED DEMAND SYSTEMS

To date, mixed demand systems have been all but ignored in empirical work. A possible reason for the scarcity of such applications is that one needs to know a priori which prices and quantities are endogenous in the mixed demand system. By using a directed acyclical graph (DAG), causal relationships among price and quantity variables are identified giving rise to a causally identified demand system (CIDS). A statistical comparison is made of the traditional Rotterdam model with a Rotterdam mixed demand system identified through the use of a DAG. In this analysis, the respective Rotterdam demand systems consist of five products: steak, ground beef, roast beef, pork, and chicken.Demand and Price Analysis,