On modeling cognitive and affective factors in legal decision-making

In recent years, many empirical studies of legal decision-making process have shown that it incorporates many cognitive, affective, and supra-legal factors. Our goal is to design artificial intelligence systems that model these aspects of legal decision-making. Our vision is to implement a kind of legal assistant that can be used by lawyers and judges to run through different scenarios and produce arguments for different, and possibly contradictory, decisions. We propose a multi-agent blackboard architecture for such an assistive system, employing some insights from our previous work on a context-aware recommender system

And If Your Friends Jumped Off A Bridge, Would You Do It Too? : How Developmental Neuroscience Can Inform Legal Regimes Governing Adolescents

Legal models of adolescent autonomy and responsibility in various domains of law span a spectrum from categorical prohibitions of certain behaviors to recognitions of total adolescent autonomy. The piecemeal approach to the limited decision-making capacity of adolescents lacks an empirical foundation in the differences between adolescent and adult decision-making, leading to counterintuitive and inconsistent legal outcomes. The law limits adolescent autonomy with respect to some decisions that adolescents are perfectly competent to make, and in other areas, the law attributes adult responsibility and imposes adult punishments on adolescents for making decisions that implicate their unique volitional vulnerabilities. As developmental neuroscientists discover more about the biological underpinnings of juvenile decision-making, policymakers now have the opportunity to enhance consistency within and across the legal domains that regulate adolescent behavior. To serve this goal, our paper typologizes extant legal regimes that account for the limitations of adolescent decision making, reviews the neuroscientific evidence about how the brain’s developing structures and functions affect decision making, explores case studies of how certain youth behaviors that implicate the adolescent brain’s unique vulnerabilities intersect with the legal system, and proposes a matrix-based approach for the consistent legal evaluation of adolescent behavior

Modeling Option and Strategy Choices with Connectionist Networks: Towards an Integrative Model of Automatic and Deliberate Decision Making

We claim that understanding human decisions requires that both automatic and deliberate processes be considered. First, we sketch the qualitative differences between two hypothetical processing systems, an automatic and a deliberate system. Second, we show the potential that connectionism offers for modeling processes of decision making and discuss some empirical evidence. Specifically, we posit that the integration of information and the application of a selection rule are governed by the automatic system. The deliberate system is assumed to be responsible for information search, inferences and the modification of the network that the automatic processes act on. Third, we critically evaluate the multiple-strategy approach to decision making. We introduce the basic assumption of an integrative approach stating that individuals apply an all-purpose rule for decisions but use different strategies for information search. Fourth, we develop a connectionist framework that explains the interaction between automatic and deliberate processes and is able to account for choices both at the option and at the strategy level.System 1, Intuition, Reasoning, Control, Routines, Connectionist Model, Parallel Constraint Satisfaction

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Research and Applications of the Processes of Performance Appraisal: A Bibliography of Recent Literature, 1981-1989

[Excerpt] There have been several recent reviews of different subtopics within the general performance appraisal literature. The reader of these reviews will find, however, that the accompanying citations may be of limited utility for one or more reasons. For example, the reference sections of these reviews are usually composed of citations which support a specific theory or practical approach to the evaluation of human performance. Consequently, the citation lists for these reviews are, as they must be, highly selective and do not include works that may have only a peripheral relationship to a given reviewer\u27s target concerns. Another problem is that the citations are out of date. That is, review articles frequently contain many citations that are fifteen or more years old. The generation of new studies and knowledge in this field occurs very rapidly. This creates a need for additional reference information solely devoted to identifying the wealth of new research, ideas, and writing that is changing the field

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Trust and the Decision to Outsource: Affective Responses and Cognitive Processes

Many of the various forms of cooperative strategy that firms are pursuing in today's economy entail the placing of important business functions in the hands of a partner. This paper examines the role of trust in the decision by a producer to place the marketing function in the hands of another entity, namely a cooperative. Although others have studied the effect of what may be termed general trust on inter-organizational relationships, few have examined the antecedents of that trust. We propose a model in which affective responses and cognitive processes are precursors to a sense of general trust, which, in turn, influences the outsourcing decision. These affective responses and cognitive processes have both direct and indirect (mediated) effects on the decision to place an important function in the hands of another entity. Perceptions of partner expertise in the business function at hand and the perceived need for the focal firm to maintain control over that function are also considered in the model. The model is tested in a somewhat novel context: the decision of cotton producers to outsource the marketing of their cotton fiber. Using survey data gathered from the actual decision-maker, and structural equations modeling, we find that the inclusion of affective responses and cognitive processes in our model produces a richer explanation of the outsourcing decision. The differences between the effects of affective responses and cognitive processes have potentially important implications for managers engaged in cooperative strategies and for the scholars who study them.Agribusiness,