Research On The Application Of Explanatory Coherence And Bayesian Networks In Judicial Judgment

Bayesian network and explanatory coherence are two kinds of causal reasoning model.Bayesian network is a causal calculation model based on probability,while explanatory coherence is based on coupling and ECHO.In this paper,we make a case study of judicial judgment using Bayesian networks and explanatory coherence theory by the case of the Murder of von Bulow.Fair is one of the objectives pursued by the law.In order to achieve legal justice,we must rely on legal rationality.The judge needs to make decisions according to the law,while the jury has to make judgement for the case based on their own values and the simple moral sense of critical thinking.In von Bulow case,jurors based on their simple thought process,in first and second instance respectively make different inferences based on the fact that different evidence.Logic can help people achieve legal certainty,legal uncertainty and there at the same time,the legal uncertainty as a logical method provides the possibility of judicial decisions in the reasoning of the application.The judges want to explore the reasons for the decision to resolve the current case law under conditions of uncertainty.Thagard twice through his summary of the trial jurors and the judge ruled that evidence of a causal inference.The Bayesian rule is a method created by Bayesian to modify the subjective judgment of the probability associated with the observed phenomenon,which constitutes the logical basis of Bayesian reasoning.Bayesian reasoning is the Bayesian law in the uncertain conditions,has been widely used in forming judicial decisions.Bayesian network is the application of Bayesian reasoning.It is a computational model of the relationship between variables in a probabilistic probability network,including Bayesian network learning and Bayesian network reasoning.The Bayesian network consists of Bayesian network structure and conditional probability table.Bayesian network learning includes causal structure learning and probabilistic table presentation of parametric learning.The process of judicial decision is the process of legal decision-making.Its essence is the process of collecting information.If the information is complete,it is not difficult to make the decision.The Bayesian network modeling and application analysis are carried out by using von Bulow as an example to verify the feasibility and limitations of Bayesian network in judicial decision.Thagard defines his coherence as "constraint satisfaction".Explanatory coherence is one of the ways to achieve coherence.Thagard's theory evaluation model has an operational platform to analyze the change mechanism of propositional system in scientific theory by using the theory of coherence,and make the coherent calculation realizable by using the executable concatenation algorithm(ECHO computer program).The theory of coherence includes seven principles of symmetry,interpretation,analogy,data priority,contradiction,competition and acceptance.In order to apply the explanatory coherence theory and the ECHO calculation model to the case of von Bulow,it is necessary to express the causal relationship in the part of the corollary.The ECHO model needs to input such as((H1 H2)E1),i.e.assuming that H1 and H2 together interpret the evidence E1.ECHO program successfully simulated the first jury and the second jury.Explanatory coherence theory shows how to integrate different types of causal elements to determine whether the defendant is innocent.Bayesian network can be calculated through the network to perform,the theory of explain coherence also shows that the explanatory of the degree of coherence in the proposition network through the connection process ECHO operation.By the application of the case of von Bulow,we conclude that the coherence is more suitable for the application of the judiciary.