Emergent Reasoning Structures in Law

Emergent Reasoning Structures in Law

Vern R. Walker (Hofstra University, USA)
DOI: 10.4018/978-1-60566-236-7.ch021
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In modern legal systems, a large number of autonomous agents can achieve reasonably fair and accurate decisions in tens of thousands of legal cases. In many of those cases, the issues are complicated, the evidence is extensive, and the reasoning is complex. The decision-making process also integrates legal rules and policies with expert and non-expert evidence. This chapter discusses two major types of reasoning that have emerged to help bring about this remarkable social achievement: systems of rule-based deductions and patterns of evidence evaluation. In addition to those emergent structures, second-order reasoning about legal reasoning itself not only coordinates the decision-making, but also promotes the emergence of new reasoning structures. The chapter analyzes these types of reasoning structures using a many-valued, predicate, default logic – the Default-Logic (D-L) Framework. This framework is able to represent legal knowledge and reasoning in actual cases, to integrate and help evaluate expert and non-expert evidence, to coordinate agents working on different legal problems, and to guide the evolution of the knowledge model over time. The D-L Framework is also useful in automating portions of legal reasoning, as evidenced by the Legal Apprenticetm software. The framework therefore facilitates the interaction of human and non-human agents in legal decision- making, and makes it possible for non-human agents to participate in the evolution of legal reasoning in the future. Finally, because the D-L Framework itself is grounded in logic and not on theories peculiar to the legal domain, it is applicable to other knowledge domains that have a complexity similar to that of law and solve problems through default reasoning.
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The logical structure of legal reasoning, and especially its second-order reasoning about the reasoning process itself, is a primary mechanism by which new legal rules and new plausibility schemas emerge, and through which such rules and schemas adapt to the nuances of legal cases. This reasoning structure not only coordinates the efforts of numerous autonomous agents, but also promotes the emergence and evolution of new reasoning structures by responding to the tremendous variability provided by individual legal cases. This chapter describes the Default-Logic (D-L) Framework, which accurately models the logical structure of legal reasoning in actual legal cases. Moreover, it is the logical structure of legal reasoning itself, and not any particular set of rules within the legal knowledge domain, that creates this evolutionary mechanism. This means that the evolutionary mechanism captured by the D-L Framework can operate in domestic, foreign and international legal systems; that non-human autonomous agents can participate in this evolution, interacting with human agents; and that similar reasoning structures can operate in many knowledge domains other than law.

Legal reasoning is a distinctive method of reasoning that has emerged because of adherence to the rule of law. The rule of law requires that similar cases should be decided similarly, that each case should be decided on its merits, and that decision-making processes should comply with all applicable legal rules. One safeguard for achieving these fundamental goals is to make the reasoning behind legal decisions transparent and open to scrutiny. If the legal rules and policies are the same between cases, and the evidence and reasoning in particular cases are publicly available and subject to scrutiny, then the legal decisions in those cases are more likely to be evidence-based and consistent. Transparency makes the decisions less likely to be merely subjective, and more likely to have an objective rationale. An important means of achieving the rule of law, therefore, is articulating and scrutinizing the various elements of the reasoning exhibited in legal cases. Such reasoning involves interpreting constitutions, statutes, and regulations, balancing legal principles and policies, adopting and refining legal rules, adapting those rules to particular cases, evaluating the evidence in each case, and making ultimate decisions that are based on all of these elements.

Legal decision-making today requires many agents performing many different tasks. As the number and diversity of legal cases has increased, and the legal issues in those cases have become more specialized, it has become necessary to distribute the functions needed for optimal decision-making over more and more agents. First, these agents include the specialists in the law itself – the law-makers (legislators, regulators, and judges), the law-appliers (such as judges and administrative personnel), and the advocates using the law (the lawyers representing parties). Such agents, either individually or in groups, establish the legal rules (e.g., by enacting statutes or issuing regulations), clarify their meaning (e.g., when deciding motions), and ensure that the rules are applied in appropriate cases (e.g., by advocating for particular outcomes, rules and policies). Second, there are the agents (witnesses) who supply the evidence needed to apply the legal rules accurately. Some witnesses have personal knowledge of disputed issues of fact. Other witnesses are experts who have scientific, technical, or other specialized knowledge that is relevant in particular cases – for example, knowledge about forensic science, product testing, medical care or engineering. Such agents supply the evidence needed to apply the legal rules accurately. Third, there are agents who act as the “factfinders.” Depending upon the nature of the proceeding, a jury, judge, or administrative official listens to the witnesses, reads the relevant documents, evaluates all of the evidence, and decides what that evidence establishes as the “facts” for legal purposes. In modern legal systems, with tens of thousands of legal cases, a very large number of autonomous human agents participate, and they together achieve reasonably fair and accurate decisions. This achievement is possible because the reasoning in those cases is organized and supervised under the rule of law; the law, evidence and reasoning are transparent and publicly available; and the decision-making processes are open to scrutiny.

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List of Reviewers
Table of Contents
Georgi Stojanov
Chapter 1
R. Keith Sawyer
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The Science of Social Emergence
Chapter 2
Christopher Goldspink, Robert Kay
This chapter critically examines our theoretical understanding of the dialectical relationship between emergent social structures and agent... Sample PDF
Agent Cognitive Capabilities and Orders of Social Emergence
Chapter 3
Joseph C. Bullington
Social interaction represents a powerful new locus of research in the quest to build more truly human-like artificial agents. The work in this area... Sample PDF
Agents and Social Interaction: Insights from Social Psychology
Chapter 4
M. Afzal Upal
This chapter will critically review existing approaches to the modeling transmission of cultural information and advocate a new approach based on a... Sample PDF
Predictive Models of Cultural Information Transmission
Chapter 5
Jorge A. Romero
Despite the popularity of agents for the information technology infrastructure, questions remain because it is not clear what do e-business agents... Sample PDF
Interaction of Agent in E-Business: A Look at Different Sources
Chapter 6
Adam J. Conover
This chapter presents a description of ongoing experimental research into the emergent properties of multi-agent communication in “temporally... Sample PDF
A Simulation of Temporally Variant Agent Interaction via Passive Inquiry
Chapter 7
Richard Schilling
This chapter presents a generalized messaging infrastructure that can be used for distributed agent systems. The principle of agent feedback... Sample PDF
Agent Feedback Messaging: A Messaging Infrastructure for Distributed Message Delivery
Chapter 8
Yu Zhang, Mark Lewis, Christine Drennon, Michael Pellon, Coleman
Multi-agent systems have been used to model complex social systems in many domains. The entire movement of multi-agent paradigm was spawned, at... Sample PDF
Modeling Cognitive Agents for Social Systems and a Simulation in Urban Dynamics
Chapter 9
Scott Watson, Kerstin Dautenhahn, Wan Ching (Steve) Ho, Rafal Dawidowicz
This chapter discusses certain issues in the development of Virtual Learning Environments (VLEs) populated by autonomous social agents, with... Sample PDF
Developing Relationships Between Autonomous Agents: Promoting Pro-Social Behaviour Through Virtual Learning Environments Part I
Chapter 10
Martin Takác
In this chapter, we focus on the issue of understanding in various types of agents. Our main goal is to build up notions of meanings and... Sample PDF
Construction of Meanings in Biological and Artificial Agents
Chapter 11
Myriam Abramson
In heterogeneous multi-agent systems, where human and non-human agents coexist, intelligent proxy agents can help smooth out fundamental... Sample PDF
Training Coordination Proxy Agents Using Reinforcement Learning
Chapter 12
Deborah V. Duong
The first intelligent agent social model, in 1991, used tags with emergent meaning to simulate the emergence of institutions based on the principles... Sample PDF
The Generative Power of Signs: The Importance of the Autonomous Perception of Tags to the Strong Emergence of Institutions
Chapter 13
Josefina Sierra, Josefina Santibáñez
This chapter addresses the problem of the acquisition of the syntax of propositional logic. An approach based on general purpose cognitive... Sample PDF
Propositional Logic Syntax Acquisition Using Induction and Self-Organisation
Chapter 14
Giovanni Vincenti, James Braman
Emotions influence our everyday lives, guiding and misguiding us. They lead us to happiness and love, but also to irrational acts. Artificial... Sample PDF
Hybrid Emotionally Aware Mediated Multiagency
Chapter 15
Samuel G. Collins, Goran Trajkovski
In this chapter, we give an overview of the results of a Human-Robot Interaction experiment, in a near zerocontext environment. We stimulate the... Sample PDF
Mapping Hybrid Agencies Through Multiagent Systems
Chapter 16
Scott Watson, Kerstin Dautenhahn, Wan Ching (Steve) Ho, Rafal Dawidowicz
This chapter is a continuation from Part I, which has described contemporary psychological descriptions of bullying in primary schools and two... Sample PDF
Developing Relationships Between Autonomous Agents: Promoting Pro-Social Behaviour Through Virtual Learning Environments Part II
Chapter 17
Mario Paolucci, Rosaria Conte
This chapter is focused on social reputation as a fundamental mechanism in the diffusion and possibly evolution of socially desirable behaviour... Sample PDF
Reputation: Social Transmission for Partner Selection
Chapter 18
Adam J. Conover
This chapter concludes a two part series which examines the emergent properties of multi-agent communication in “temporally asynchronous”... Sample PDF
A Simulation of Temporally Variant Agent Interaction via Belief Promulgation
Chapter 19
David B. Newlin
Following the discovery in Rhesus monkeys of “mirror neurons” that fire during both execution and observation of motor behavior, human studies have... Sample PDF
The Human Mirror Neuron System
Chapter 20
Eric Baumer, Bill Tomlinson
This chapter presents an argument that the process of emergence is the converse of the process of abstraction. Emergence involves complex behavior... Sample PDF
Relationships Between the Processes of Emergence and Abstraction in Societies
Chapter 21
Vern R. Walker
In modern legal systems, a large number of autonomous agents can achieve reasonably fair and accurate decisions in tens of thousands of legal cases.... Sample PDF
Emergent Reasoning Structures in Law
Chapter 22
Theodor Richardson
Network Intrusion Detection Systems (NIDS) are designed to differentiate malicious traffic, from normal traf- fic, on a network system to detect the... Sample PDF
Agents in Security: A Look at the Use of Agents in Host-Based Monitoring and Protection and Network Intrusion Detection
Chapter 23
Michael J. North, Thomas R. Howe, Nick Collier, Eric Tatara, Jonathan Ozik, Charles Macal
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Search as a Tool for Emergence
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