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[null,"1.","Record Nr.",null,"UNINA9910782274603321"]
[null,null,"Autore",null,"Das Subrata Kumar"]
[null,null,"Titolo",null,"Foundations of decision-making agents [[electronic resource] ] : logic, probability and modality / / Subrata Das"]
[null,null,"Pubbl/distr/stampa",null,"Singapore ; ; Hackensack, NJ, : World Scientific"]
[{"$nulls":4},"[London], : Imperial College Press, c2008"]
[null,null,"ISBN",null,"1-281-93816-5"]
[{"$nulls":4},"9786611938161"]
[{"$nulls":4},"981-277-984-1"]
[null,null,"Descrizione fisica",null,"1 online resource (385 p.)"]
[null,null,"Disciplina",null,"006.33"]
[null,null,"Soggetti",null,"Intelligent agents (Computer software)"]
[{"$nulls":4},"Artificial intelligence - Computer programs"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references (p. 347-353) and index."]
[null,null,"Nota di contenuto",null,"ch. 1. Modeling agent epistemic states: an informal overview. 1.1. Models of agent epistemic states. 1.2. Propositional epistemic model. 1.3. Probabilistic epistemic model. 1.4. Possible world epistemic model. 1.5. Comparisons of models. 1.6. P3 model for decision-making agents -- ch. 2. Mathematical preliminaries. 2.1. Usage of symbols. 2.2. Sets, relations, and functions. 2.3. Graphs and trees. 2.4. Probability. 2.5. Algorithmic complexity -- ch. 3. Classical logics for the propositional epistemic model. 3.1. Propositional logic. 3.2. First-order logic. 3.3. Theorem proving procedure. 3.4. Resolution theorem proving. 3.5. Refutation procedure. 3.6. Complexity analysis -- ch. 4. Logic programming. 4.1. The concept. 4.2. Program clauses and goals. 4.3. Program semantics. 4.4. Definite programs. 4.5. Normal programs. 4.6. Prolog. 4.7. Prolog systems. 4.8. Complexity analysis -- ch. 5. Logical rules for making decisions. 5.1. Evolution of rules. 5.2. Bayesian probability theory for handling uncertainty. 5.3. Dempster-Shafer theory for handling uncertainty. 5.4. Measuring consensus. 5.5. Combining sources of varying confidence. 5.6. Advantages and disadvantages of rule-based systems -- ch. 6. Bayesian belief networks. 6.1. Bayesian belief networks. 6.2. Conditional independence in belief networks. 6.3. Evidence, belief, and likelihood. 6.4. Prior probabilities in networks without evidence. 6.5. Belief revision. 6.6. Evidence propagation in polytrees. 6.7. Evidence propagation in "]
[{"$nulls":4},"directed acyclic graphs. 6.8. Complexity of inference algorithms. 6.9. Acquisition of probabilities. 6.10. Advantages and disadvantages of belief networks. 6.11. Belief network tools -- ch. 7. Influence diagrams for making decisions. 7.1. Expected utility theory and decision trees. 7.2. Influence diagrams. 7.3. Inferencing in influence diagrams. 7.4. Compilation of influence diagrams. 7.5. Inferencing in strong junction tress -- ch. 8. Modal logics for the possible world epistemic model. 8.1. Historical development of modal logics. 8.2. Systems of modal logic. 8.3. Deductions in modal systems. 8.4. Modality. 8.5. Decidability and matrix method. 8.6. Relationships among modal systems. 8.7. Possible world semantics. 8.8. Soundness and completeness results. 8.9. Complexity and decidability of modal systems. 8.10. Modal first-order logics. 8.11. Resolution in modal first-order logics. 8.12. Modal epistemic logics. 8.13. Logic of agents beliefs (LAB) -- ch. 9. Symbolic argumentation for decision-making. 9.1. Toulmin's model of argumentation. 9.2. Domino decision-making model for P3. 9.3. Knowledge representation syntax of P3. 9.4. Formalization of P3 via LAB. 9.5. Aggregation via Dempster-Shafer theory. 9.6. Aggregation via Bayesian belief networks."]
[null,null,null,"Sommario/riassunto","This self-contained book provides three fundamental and generic approaches (logical, probabilistic, and modal) to representing and reasoning with agent epistemic states, specifically in the context of decision making. Each of these approaches can be applied to the construction of intelligent software agents for making decisions, thereby creating computational foundations for decision-making agents. In addition, the book introduces a formal integration of the three approaches into a single unified approach that combines the advantages of all the approaches. Finally, the symbolic argumentation approach to decision making developed in this book, combining logic and probability, offers several advantages over the traditional approach to decision making which is based on simple rule-based expert systems or expected utility theory."]
[null,"2.","Record Nr.",null,"UNINA9910785528503321"]
[null,null,"Autore",null,"Lund-Jensen Kasper"]
[null,null,"Titolo",null,"Monitoring Systemic Risk Basedon Dynamic Thresholds / / Kasper Lund-Jensen"]
[null,null,"Pubbl/distr/stampa",null,"Washington, D.C. : , : International Monetary Fund, , 2012"]
[null,null,"ISBN",null,"1-4755-8973-5"]
[{"$nulls":4},"1-4755-3725-5"]
[null,null,"Descrizione fisica",null,"1 online resource (37 p.)"]
[null,null,"Collana",null,"IMF Working Papers"]
[null,null,"Soggetti",null,"Financial risk management"]
[{"$nulls":4},"Risk management"]
[{"$nulls":4},"Banks and Banking"]
[{"$nulls":4},"Finance: General"]
[{"$nulls":4},"Macroeconomics"]
[{"$nulls":4},"Foreign Exchange"]
[{"$nulls":4},"General Financial Markets: Government Policy and Regulation"]
[{"$nulls":4},"Financial Crises"]
[{"$nulls":4},"Banks"]
[{"$nulls":4},"Depository Institutions"]
[{"$nulls":4},"Micro Finance Institutions"]
[{"$nulls":4},"Mortgages"]
[{"$nulls":4},"Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook: General"]
[{"$nulls":4},"Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook: Other"]
[{"$nulls":4},"Financial Markets and the Macroeconomy"]
[{"$nulls":4},"Finance"]
[{"$nulls":4},"Economic & financial crises & disasters"]
[{"$nulls":4},"Banking"]
[{"$nulls":4},"Currency"]
[{"$nulls":4},"Foreign exchange"]
[{"$nulls":4},"Systemic risk"]
[{"$nulls":4},"Systemic crises"]
[{"$nulls":4},"Commercial banks"]
[{"$nulls":4},"Systemic risk assessment"]
[{"$nulls":4},"Financial sector policy and analysis"]
[{"$nulls":4},"Financial crises"]
[{"$nulls":4},"Financial institutions"]
[{"$nulls":4},"Real effective exchange rates"]
[{"$nulls":4},"Banks and banking"]
[{"$nulls":4},"United States"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,"Nota di contenuto",null,"Cover; Contents; I. Introduction; II. Related Literature; III. Econometric Methodology and Model Specification; A. Model Specification; Figures; 1. Binary Response Model Structure; Tables; 1. Countries in Data Sample; 2. Systemic Banking Crises, 1970-2010; IV. Estimation Results; 3. Standardized Marginal Effects; 4. Systemic Risk Factors; 2. Systemic Risk Factors based on Dynamic Logit Model, 1970-2010; V. Monitoring Systemic Risk; A. The Signal Extraction Approach; 3. Signal Classification; B. Crisis signals based on binary response model; 5. Optimal Threshold"]
[{"$nulls":4},"4. Monitoring Systemic Risk, 1970-2010C. Risk Factor Thresholds; 6. Systemic Risk Estimates and Crisis Signals; 7. Credit-to-GDP Growth Threshold; D. Out-of-Sample Analysis; 5. Monitoring Systemic Risk - Out-of-Sample Analysis: 2001-2010; VI. Concluding Remarks; 8. Systemic Risk Estimates for the United States; Appendices; I. Data Sources and Description; 6. Systemic Risk Factors (1/2), 1970-2010; II. Binary Response Model Estimation Results; 7. Systemic Risk Factors (2/2), 1970-2010; 8. Systemic Risk Factors based on Dynamic Logit Model (Credit-to-GDP Growth), 1970-2010"]
[{"$nulls":4},"9. Systemic Banking Crises DatesIII. Systemic Banking Crises Dates; References"]
[null,null,null,"Sommario/riassunto","Successful implementation of macroprudential policy is contingent on the ability to identify and estimate systemic risk in real time. In this paper, systemic risk is defined as the conditional probability of a systemic banking crisis and this conditional probability is modeled in a fixed effect binary response model framework. The model structure is dynamic and is designed for monitoring as the systemic risk forecasts only depend on data that are available in real time. Several risk factors are identified and it is hereby shown that the level of systemic risk contains a predictable component which varies through time. Furthermore, it is shown how the systemic risk forecasts map into crisis signals and how policy thresholds are derived in this framework. Finally, in an out-of-sample exercise, it is shown that the systemic risk estimates provided reliable early warning signals ahead of the recent financial crisis for several economies."]