Active inference, evidence accumulation, and the urn task

Deciding how much evidence to accumulate before making a decision is a problem we and other animals often face, but one that is not completely understood. This issue is particularly important because a tendency to sample less information (often known as reflection impulsivity) is a feature in several psychopathologies, such as psychosis. A formal understanding of information sampling may therefore clarify the computational anatomy of psychopathology. In this theoretical letter, we consider evidence accumulation in terms of active (Bayesian) inference using a generic model of Markov decision processes. Here, agents are equipped with beliefs about their own behavior--in this case, that they will make informed decisions. Normative decision making is then modeled using variational Bayes to minimize surprise about choice outcomes. Under this scheme, different facets of belief updating map naturally onto the functional anatomy of the brain (at least at a heuristic level). Of particular interest is the key role played by the expected precision of beliefs about control, which we have previously suggested may be encoded by dopaminergic neurons in the midbrain. We show that manipulating expected precision strongly affects how much information an agent characteristically samples, and thus provides a possible link between impulsivity and dopaminergic dysfunction. Our study therefore represents a step toward understanding evidence accumulation in terms of neurobiologically plausible Bayesian inference and may cast light on why this process is disordered in psychopathology

Visualizing Deep Networks by Optimizing with Integrated Gradients

Understanding and interpreting the decisions made by deep learning models is valuable in many domains. In computer vision, computing heatmaps from a deep network is a popular approach for visualizing and understanding deep networks. However, heatmaps that do not correlate with the network may mislead human, hence the performance of heatmaps in providing a faithful explanation to the underlying deep network is crucial. In this paper, we propose I-GOS, which optimizes for a heatmap so that the classification scores on the masked image would maximally decrease. The main novelty of the approach is to compute descent directions based on the integrated gradients instead of the normal gradient, which avoids local optima and speeds up convergence. Compared with previous approaches, our method can flexibly compute heatmaps at any resolution for different user needs. Extensive experiments on several benchmark datasets show that the heatmaps produced by our approach are more correlated with the decision of the underlying deep network, in comparison with other state-of-the-art approaches

An optical model for an analogy of Parrondo game and designing Brownian ratchets

An optical model of classical photons propagating through array of many beam splitters is developed to give a physical analogy of Parrondo's game and Parrondo-Harmer-Abbott game. We showed both the two games are reasonable game without so-called game paradox and they are essentially the same. We designed the games with long-term memory on loop lattice and history-entangled game. The strong correlation between nearest two rounds of game can make the combination of two losing game win, lose or oscillate between win and loss. The periodic potential in Brownian ratchet is analogous to a long chain of beam splitters. The coupling between two neighboring potential wells is equivalent to two coupled beam splitters. This correspondence may help us to understand the anomalous motion of exceptional Brownian particles moving in the opposite direction to the majority. We designed the capital wave for a game by introducing correlations into independent capitals instead of sub-games. Playing entangled quantum states in many coupled classical games obey the same rules for manipulating quantum states in many body physics.Comment: 18 pages in two colum

Lobbying and the Power of Multinational Firms

Are national or multinational firms better lobbyists? This paper analyzes the extent of national environmental regulation when policy is determined in a lobbying game between a government and firm. We compare the resulting regulation levels for national and multinational firms. We identify three countervailing forces, the easier-to-shut-down effect, the easier-to-curb-exports effect and the multiple-plant effect. The interplay of these three forces determines whether national or multinational firms produce more, depending on such parameters as the potential environmental damages, transportation costs and the in uence of the firm. We also show that welfare levels are higher with multinational firms than with national firms when there is no lobbying, but that lobbying can reverse the welfare ordering.Multinational enterprises, regulation, policy formation, lobbying, interest groups, foreign direct investment

Environmental risk management system design for hazardous waste materials

Hazardous materials can be generally deemed as any material which, because of its quantity, concentration, or physical, chemical, or infectious characteristics, may cause, or pose a substantial or potential hazard to human health or the environment. In the context of "sustainable development", most 'materials' could be deemed to be 'hazardous' at some stage of their lifecycle, i.e. from extraction to final disposal.This PhD study develops a decision support system for engineers and policy makers to help limit environmental burden, by reducing the environmental risk and the associated carbon footprint, from the perspective of 'hazardous' materials in product design, through the application of 'game theory' and 'grey theory' etc, as well as various computational approaches, by helping the designer identify novel solutions or mitigation strategies.The thesis starts by introducing the problem situation of the study and identify the research objectives, as well as previous studies have been reviewed in order to set this study in context.Since it is evident that consumers drive the open market, and their preference may be influenced by the carbon footprint label of products, the decision support system proposes an improved carbon labelling scheme to demonstrate the significance of a product‘s carbon footprint in a more visual way. The prototype of the scheme is derived from the concept of 'tolerability of risk', providing a framework by which judgments can be made as to whether society will accept the risk from hazardous materials.Application of game theory for decision support is a novel approach in this study, which aids decision-making by selecting appropriate strategies for both organisations and policy makers to reduce environmental impact. In this context, a game between manufacturers and government in the field of clean production is generated with various game scenarios to reflect the variation trend of strategic actions, and then developed to discuss the reduction of the inherent risk posed by 'hazardous' materials and carbon emissions on the supply chain network.The 'hierarchy of waste' suggests that the most preferable state for sustainability is prevention or the elimination of waste. Although this is not wholly practicable in real terms, the framework gives the importance to waste minimisation and prevention, especially promotes the cleaner production. In addition to strategy selection for mitigating environmental impact, the decision support system also develops an evaluation methodology for application by engineers to aid decision-making on materials selection, thus to improve the materials performances, promote cleaner production and provide better and sustainable products for public consumption