A Survey on Growth and Inequality: Does Improved Inequality Data Have Anything to Say?

Theories on the relationship between inequality and economic growth can be divided into two strands of paradigm, i.e. those which predict tradeoff between growth and equity, and those which predict no tradeoff. The consensus of empirical literature in 1980s until mid 1990s suggest there need be no conflict between fast growth and distribution. Empirical works in that era, however, were subject to criticism over the reliability of inequality data. The availability and accessibility of more improved income inequality data after the publication of Deininger and Squire’s (1996) had motivated more empirical works on the relationship between growth and inequality and had also made possible the use of relatively advanced econometric methods. Recent empirical literature following this publication of new dataset, however, do not provide strong support for whether growth and inequality are negatively or positively associated. It mainly suggest no overall relation between growth and inequality. There is little indication, however, that in the context of developing countries, the tradeoff may be resolved.growth, inequality

(WP 2020-01) The Sea Battle Tomorrow: The Identity of Reflexive Economic Agents

This paper develops a conception of reflexive economic agents as an alternative to the standard utility conception, and explains individual identity in terms of how agents adjust to change in a self-organizing way, an idea developed from Herbert Simon. The paper distinguishes closed equilibrium and open process conceptions of the economy, and argues the former fails to explain time in a before-and-after sense in connection with Aristotle’s sea battle problem. A causal model is developed to represent the process conception, and a structure-agency understanding of the adjustment behavior of reflexive economic agents is illustrated using Merton’s self-fulfilling prophecy analysis. Simon’s account of how adjustment behavior has stopping points is then shown to underlie how agents’ identities are disrupted and then self-organized, and the identity analysis this involves is applied to the different identity models of Merton, Ross, Arthur, and Kirman. Finally, the self-organization idea is linked to the recent ‘preference purification’ debate in bounded rationality theory regarding the ‘inner rational agent trapped in an outer psychological shell,’ and it is argued that the behavior of self-organizing agents involves them taking positions toward their own individual identities

Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective

Advances in the use of neuroimaging in combination with A.I., and specifically the use of machine learning techniques, have led to the development of brain-reading technologies which, in the nearby future, could have many applications, such as lie detection, neuromarketing or brain-computer interfaces. Some of these could, in principle, also be used in forensic psychiatry. The application of these methods in forensic psychiatry could, for instance, be helpful to increase the accuracy of risk assessment and to identify possible interventions. This technique could be referred to as ‘A.I. neuroprediction,’ and involves identifying potential neurocognitive markers for the prediction of recidivism. However, the future implications of this technique and the role of neuroscience and A.I. in violence risk assessment remain to be established. In this paper, we review and analyze the literature concerning the use of brain-reading A.I. for neuroprediction of violence and rearrest to identify possibilities and challenges in the future use of these techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues. The analysis suggests that additional research is required on A.I. neuroprediction techniques, and there is still a great need to understand how they can be implemented in risk assessment in the field of forensic psychiatry. Besides the alluring potential of A.I. neuroprediction, we argue that its use in criminal justice and forensic psychiatry should be subjected to thorough harms/benefits analyses not only when these technologies will be fully available, but also while they are being researched and developed

ReLoC Reloaded:A Mechanized Relational Logic for Fine-Grained Concurrency and Logical Atomicity

We present a new version of ReLoC: a relational separation logic for proving refinements of programs with higher-order state, fine-grained concurrency, polymorphism and recursive types. The core of ReLoC is its refinement judgment $e \precsim e' : \tau$, which states that a program $e$ refines a program $e'$ at type $\tau$. ReLoC provides type-directed structural rules and symbolic execution rules in separation-logic style for manipulating the judgment, whereas in prior work on refinements for languages with higher-order state and concurrency, such proofs were carried out by unfolding the judgment into its definition in the model. ReLoC's abstract proof rules make it simpler to carry out refinement proofs, and enable us to generalize the notion of logically atomic specifications to the relational case, which we call logically atomic relational specifications. We build ReLoC on top of the Iris framework for separation logic in Coq, allowing us to leverage features of Iris to prove soundness of ReLoC, and to carry out refinement proofs in ReLoC. We implement tactics for interactive proofs in ReLoC, allowing us to mechanize several case studies in Coq, and thereby demonstrate the practicality of ReLoC. ReLoC Reloaded extends ReLoC (LICS'18) with various technical improvements, a new Coq mechanization, and support for Iris's prophecy variables. The latter allows us to carry out refinement proofs that involve reasoning about the program's future. We also expand ReLoC's notion of logically atomic relational specifications with a new flavor based on the HOCAP pattern by Svendsen et al