Reason is too large : analogy and precedent in law

Hunter argues that cognitive science models of human thinking explain how analogical reasoning and precedential reasoning operate in law. He offers an explanation of why various legal theories are so limited and calls for greater attention to what is actually happening when lawyers and judges reason, by analogy, with precedent.

A jurisprudential model for sustainable water resources governance

The legal arrangements for the management of water resources are currently a complex matrix of rules of various kinds. These rules perform a diverse range of functions. Some are part of what may be described as the macro-legal system for the governance of water resources. This includes paralegal rules in the form of statements of value, objective, outcome or principles . Others are part of the micro-legal system for the governance of water resources. This includes traditional legal rules in the form of statements of standards in relation to individual conduct, behaviour or decision making. These legal arrangements may be international, regional, national or local. Accordingly some apply to nation states within the international community. Others apply to the regulatory agencies making decisions about water resources within nation states. Ultimately most of these legal arrangements apply to those who use and develop water resources for particular purposes and in particular locations. In accordance with this framework, rules explain how water resources should be used in particular circumstances and how decisions should be made to ensure the effective planning and regulation of water resources.

Property : an instrumental institution for environmental governance

Property in an elusive concept. In many respects it has been regarded as a source of authority to use, develop and make decisions about whatever is the subject matter of this right of ownership. This is true whether the holder of this right of ownership is a private entity or a public entity. Increasingly a right of ownership of this kind has been recognised not only as a source of authority but also as a mechanism for restricting or limiting and perhaps even prohibiting existing or proposed activities that impact upon the environment. It is increasingly therefore an instrument of control as much as an instrument of authorisation. The protection and conservation of the environment are ultimately a matter of the public interest. This is not to suggest that the individual holders of rights of ownership are not interested in protecting the environment. It is open to them to do so in the exercise of a right of ownership as a source of authorisation. However a right of ownership – whether private or public – has become increasingly the instrument according to which the environment is protected and conserved. This article addresses these issues from a doctrinal as well as a practical perspective about how the environment is managed. It does so in five ways: ●considering briefly property as a concept ●reviewing property in its historical context ●analysing property as a human right ●examining property in natural resources ●reviewing judicial approaches to property in natural resources.

Don't think but look : a Gestalt interactionist approach to legal thinking

We propose here a new approach to legal thinking that is based on principles of Gestalt perception. Using a Gestalt view of perception, which sees perception as the process of building a conceptual representation of the given stimulus, we articulate legal thinking as the process of building a representation for the given facts of a case. We propose a model in which top-down and bottom-up processes interact together to build arguments (or representations) in legal thinking. We discuss some implications of our approach, especially with respect to modeling precedential reasoning and creativity in legal thinking.

Out of their minds: legal theory in neural networks

This paper examines the use of connectionism (neural networks) in modelling legal reasoning. I discuss how the implementations of neural networks have failed to account for legal theoretical perspectives on adjudication. I criticise the use of neural networks in law, not because connectionism is inherently unsuitable in law, but rather because it has been done so poorly to date. The paper reviews a number of legal theories which provide a grounding for the use of neural networks in law. It then examines some implementations undertaken in law and criticises their legal theoretical naïvete. It then presents a lessons from the implementations which researchers must bear in mind if they wish to build neural networks which are justified by legal theories.

The IKBALS project: multi-modal reasoning in legal knowledge based systems

In attempting to build intelligent litigation support tools, we have moved beyond first generation, production rule legal expert systems. Our work integrates rule based and case based reasoning with intelligent information retrieval. When using the case based reasoning methodology, or in our case the specialisation of case based retrieval, we need to be aware of how to retrieve relevant experience. Our research, in the legal domain, specifies an approach to the retrieval problem which relies heavily on an extended object oriented/rule based system architecture that is supplemented with causal background information. We use a distributed agent architecture to help support the reasoning process of lawyers. Our approach to integrating rule based reasoning, case based reasoning and case based retrieval is contrasted to the CABARET and PROLEXS architectures which rely on a centralised blackboard architecture. We discuss in detail how our various cooperating agents interact, and provide examples of the system at work. The IKBALS system uses a specialised induction algorithm to induce rules from cases. These rules are then used as indices during the case based retrieval process. Because we aim to build legal support tools which can be modified to suit various domains rather than single purpose legal expert systems, we focus on principles behind developing legal knowledge based systems. The original domain chosen was theAccident Compensation Act 1989 (Victoria, Australia), which relates to the provision of benefits for employees injured at work. For various reasons, which are indicated in the paper, we changed our domain to that ofCredit Act 1984 (Victoria, Australia). This Act regulates the provision of loans by financial institutions. The rule based part of our system which provides advice on the Credit Act has been commercially developed in conjunction with a legal firm. We indicate how this work has lead to the development of a methodology for constructing rule based legal knowledge based systems. We explain the process of integrating this existing commercial rule based system with the case base reasoning and retrieval architecture.

Reasoning paradigms in legal decision support systems

In this paper we discuss the strengths and weaknesses of a range of artificial intelligence approaches used in legal domains. Symbolic reasoning systems which rely on deductive, inductive and analogical reasoning are described and reviewed. The role of statistical reasoning in law is examined, and the use of neural networks analysed. There is discussion of architectures for, and examples of, systems which combine a number of these reasoning strategies. We conclude that to build intelligent legal decision support systems requires a range of reasoning strategies.

'Look and feel' computer copyright in the United States and Australia (part 2)

Australian law similar to that of United States -- Australian law requires copyright must subsist in plaintiff's material and defendent's work must infringe plaintiff's copyright to find defendent liable for illegal copying -- subsistence -- infringement -- two cases that touch on 'look and feel' issue -- passing-off -- look and feel of computer program deserves protection

Near knowledge : inductive learning systems in law

Induction is an interesting model of legal reasoning, since it provides a method of capturing initial states of legal principles and rules, and adjusting these principles and rules over time as the law changes. In this article I explain how Artificial Intelligence-based inductive learning algorithms work, and show how they have been used in law to model legal domains. I identify some problems with implementations undertaken in law to date, and create a taxonomy of appropriate cases to use in legal inductive inferencing systems. I suggest that inductive learning algorithms have potential in modeling law, but that the artificial intelligence implementations to date are problematic. I argue that induction should be further investigated, since it has the potential to be an extremely useful mechanism for understanding legal domains.

Beyond rule based reasoning - the meaning and use of cases

Commercial legal expert systems are invariably rule based. Such systems are poor at dealing with open texture and the argumentation inherent in law. To overcome these problems we suggest supplementing rule based legal expert systems with case based reasoning or neural networks. Both case based reasoners and neural networks use cases-but in very different ways. We discuss these differences at length. In particular we examine the role of explanation in existing expert systems methodologies. Because neural networks provide poor explanation facilities, we consider the use of Toulmin argument structures to support explanation (S. Toulmin, 1958). We illustrate our ideas with regard to a number of systems built by the authors