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This paper presents the VDM 2000, a computational model of target detection designed for use in military developmental test and evaluation settings. The model integrates research results from the fields of early vision, object recognition, and psychophysics. The VDM2000 is image based and provides a criterion-independent measure of target conspicuity, referred to as the vehicle metric (VM). A large data set of human responses to photographs of military vehicles in a field setting was used to validate the model. The VM adjusted by a single calibration parameter accounts for approximately 80% of the variance in the validation data. The primary application of this model is to predict detection of military targets in daylight with the unaided eye. The model also has application to target detection prediction using infrared night vision systems. The model has potential as a tool to evaluate the visual properties of more general task settings.

Policy analysis, the scientific evaluation of policy impact, must include both the technical transformation and political decision process. This analysis is plagued by limited data that leads to model uncertainty. Not only the derived models are uncertain, but also political decision-makers have to deal with this. They form simple mental models, policy beliefs. Therefore, a political economy equilibrium framework, the CGPE model, is developed. The CGPE models the political and economic system together and allows the disentanglement of political performance gaps into knowledge and incentive gaps. Structural model uncertainty is handled by a large simulation sample, while for parameter uncertainty, a MCMC sample is derived. A distributed simulation tool has been developed. A metamodeling approach is applied to model the transformation of economic growth into outcomes. Sector-specific policy impact functions are estimated using observational and expert data in a Bayesian estimation framework. We applied this framework empirically to the case of the CAADP in Ghana, Senegal, and Uganda. Indicators for key sectors, key policies, and optimal policies are derived, and the impact of model uncertainty on them is assessed. A theoretical framework for measurement and evaluation of participatory network structures is developed. The network generating process is estimated using exponential random graph models, and separate measures for lobbying and informational influence are derived. By combining this, individual policy beliefs are estimated, and their political performance gaps are measured and disentangled into knowledge and incentive gaps. The central results of these applications are: In the technical evaluation, model uncertainty is important, as derived messages can change dramatically. Designing efficient participation structures is hard. Beyond biased incentive gaps, biased beliefs are important for the observed political performance gaps. Using different constitutional and participation scenarios, we show that such a design does not solve the performance gaps. A way out are transdisciplinary research approaches, as they connect the science world with the society, and in doing so, new knowledge is generated.

AbstractComputational modeling should play a central role in philosophy. In this introduction to our topical collection, we propose a small topology of computational modeling in philosophy in general, and show how the various contributions to our topical collection fit into this overall picture. On this basis, we describe some of the ways in which computational models from other disciplines have found their way into philosophy, and how the principles one found here still underlie current trends in the field. Moreover, we argue that philosophers contribute to computational modeling not only by building their own models, but also by thinking about the various applications of the method in philosophy and the sciences. In this context, we note that models in philosophy are usually simple, while models in the sciences are often more complex and empirically grounded. Bridging certain methodological gaps that arise from this discrepancy may prove to be challenging and fruitful for the further development of computational modeling in philosophy and beyond.

Agent-based computational modeling is changing the face of social science. In Generative Social Science, Joshua Epstein argues that this powerful, novel technique permits the social sciences to meet a fundamentally new standard of explanation, in which one "grows" the phenomenon of interest in an artificial society of interacting agents: heterogeneous, boundedly rational actors, represented as mathematical or software objects. After elaborating this notion of generative explanation in a pair of overarching foundational chapters, Epstein illustrates it with examples chosen from such far-flung

"Agent-Based Computational Modeling and International Relations Theory: Quo Vadis?" published on by Oxford University Press.

This chapter is designed help motivate the current readers' interest in computational modeling as the authors provide a description of the myriad of values computational modeling can bring to our science. Readers are also given a brief history of computational modeling as it relates to the field of I-O psychology. This is followed by a more complete description of the goals for the book, as the authors describe the learning objectives for various levels of computational model afficionados, from the scholarly consumers to the computational model creators. Finally, an overview of the chapters is provided.

Theorists in management and organizational science rarely use computational modeling to support theoretical development or refinement, particularly at the micro level of analysis. This article argues that organizational scholars, who strive to understand dynamic behavior in a complex context, are particularly in need of the support computational models offer. Moreover, organizational scholars can build on (a) the plethora of informal theories extant in the literature and (b) the computational architectures and model building platforms developed in recent years. To increase the number of organizational scholars building and evaluating computational models, the article provides a tutorial in model building and simulation. Specifically, a new computational model is built and assessed. Surprising realizations emerge in the process. There is also an extensive section on model evaluation involving empirical observations.

Connectionist models represent powerful learning tools for understanding learning and development in infants and young children. In this case, we discuss two such models and how they have provided key insights into how infants and young children form perceptual object categories and how they come to represent objects in the world. Limitations of these specific models, the appeal of the connectionist modeling approach, and directions for future research are also discussed.