Hybrid automata dicretising agents for formal modelling of robots

Some of the fundamental capabilities required by autonomous vehicles and systems for their intelligent decision making are: modelling of the environment and forming data abstractions for symbolic, logic based reasoning. The paper formulates a discrete agent framework that abstracts and controls a hybrid system that is a composition of hybrid automata modelled continuous individual processes. Theoretical foundations are laid down for a class of general model composition agents (MCAs) with an advanced subclass of rational physical agents (RPAs). We define MCAs as the most basic structures for the description of complex autonomous robotic systems. The RPA’s have logic based decision making that is obtained by an extension of the hybrid systems concepts using a set of abstractions. The theory presented helps the creation of robots with reliable performance and safe operation in their environment. The paper emphasizes the abstraction aspects of the overall hybrid system that emerges from parallel composition of sets of RPAs and MCAs

Modeling Epistemological Principles for Bias Mitigation in AI Systems: An Illustration in Hiring Decisions

Artificial Intelligence (AI) has been used extensively in automatic decision making in a broad variety of scenarios, ranging from credit ratings for loans to recommendations of movies. Traditional design guidelines for AI models focus essentially on accuracy maximization, but recent work has shown that economically irrational and socially unacceptable scenarios of discrimination and unfairness are likely to arise unless these issues are explicitly addressed. This undesirable behavior has several possible sources, such as biased datasets used for training that may not be detected in black-box models. After pointing out connections between such bias of AI and the problem of induction, we focus on Popper's contributions after Hume's, which offer a logical theory of preferences. An AI model can be preferred over others on purely rational grounds after one or more attempts at refutation based on accuracy and fairness. Inspired by such epistemological principles, this paper proposes a structured approach to mitigate discrimination and unfairness caused by bias in AI systems. In the proposed computational framework, models are selected and enhanced after attempts at refutation. To illustrate our discussion, we focus on hiring decision scenarios where an AI system filters in which job applicants should go to the interview phase

The interplay between automatic and controlled processes: experimental contributions to dual-process theories of cognition

Since its beginnings, psychological science has frequently used dichotomous categories to describe behavior and mental phenomena. The most traditional dual models have impactfully equipped both the scientific and folkloristic psychological vocabularies of such dichotomies (e.g., conscious vs. unconscious, logic vs. creative, rational vs. emotional). However, while offering an affordable account of how the human cognitive system works, these models appear too simplistic. Substantially, they are grounded upon the findings obtained in decades of results in almost all the psychological fields, from perception to social processes, which have been later merged into a broad systemic theory of human cognition. However, this dual-system theory, which proposed to unify all cognitive dualities into System 1 (automatic, unconscious, fast, effortless, intuitive, and so on) and System 2 (controlled, conscious, slow, effortful, rational, and so on) entities, lacks a systematic investigation of its basic assumptions: for instance, that the features are aligned within and complementary between the two systems. These properties are essential for the tenets of the theory since a systemic theory should postulate the interdependence and interrelation of the elements constituting a system. In this view, the central thread linking all the experimental contributions in the present work is that the dual-system theory should resist when investigating cognitive performance either at low- and at high-level of complexity (complexity defined as the variety of mechanisms implicated in the phenomena of interest). Through seven studies conducted in three research lines, addressing temporal attention, task-switching, and decision-making, the interaction between automatic and controlled features in each process has shown to be the rule rather than the exception. Thus, the results presented in this work support the idea that the dual-system theory current formulation has a weak explanatory power, suggesting that decomposition approaches aimed at disentangling the contribution of qualitatively and quantitatively different mechanisms in each cognitive process are needed to advance or put aside dual-process theories

DISCRET: An Interactive Decision Support System for Discrete Alternatives Multicriteria Problems

This paper is one of the series of 11 Working Papers presenting the software for interactive decision support and software tools for developing decision support systems. These products constitute the outcome of the contracted study agreement between the System and Decision Sciences Program at IIASA and several Polish scientific institutions. The theoretical part of these results is presented in the IIASA Working Paper WP-88-071 entitled "Theory, Software and Testing Examples in Decision Support Systems" which contains the theoretical and methodological backgrounds of the software systems developed within the project. This paper presents the DISCRET system. This system has been designed to solve basic multicriteria choice problems in which a finite set of feasible alternatives is explicitly given and for each alternative the value of all criteria are known. The decision maker is assumed to be rational in the sense that he can accept a Pareto-optimal solution as his final solution of the problem. Such a decision problem is rather simple as long as the number of criteria and alternatives is small. However, if the number of alternatives and/or criteria grows, the human information processing capabilities may reach their limits and therefore decision support facilities need to be utilized to guarantee efficient decision making

Simple rules for evidence translation in complex systems: a qualitative study

Background Ensuring patients benefit from the latest medical and technical advances remains a major challenge, with rational-linear and reductionist approaches to translating evidence into practice proving inefficient and ineffective. Complexity thinking, which emphasises interconnectedness and unpredictability, offers insights to inform evidence translation theories and strategies. Drawing on detailed insights into complex micro-systems, this research aimed to advance empirical and theoretical understanding of the reality of making and sustaining improvements in complex healthcare systems. Methods Using analytical auto-ethnography, including documentary analysis and literature review, we assimilated learning from 5 years of observation of 22 evidence translation projects (UK). We used a grounded theory approach to develop substantive theory and a conceptual framework. Results were interpreted using complexity theory and ‘simple rules’ were identified reflecting the practical strategies that enhanced project progress. Results The framework for Successful Healthcare Improvement From Translating Evidence in complex systems (SHIFT-Evidence) positions the challenge of evidence translation within the dynamic context of the health system. SHIFT-Evidence is summarised by three strategic principles, namely (1) ‘act scientifically and pragmatically’ – knowledge of existing evidence needs to be combined with knowledge of the unique initial conditions of a system, and interventions need to adapt as the complex system responds and learning emerges about unpredictable effects; (2) ‘embrace complexity’ – evidence-based interventions only work if related practices and processes of care within the complex system are functional, and evidence-translation efforts need to identify and address any problems with usual care, recognising that this typically includes a range of interdependent parts of the system; and (3) ‘engage and empower’ – evidence translation and system navigation requires commitment and insights from staff and patients with experience of the local system, and changes need to align with their motivations and concerns. Twelve associated ‘simple rules’ are presented to provide actionable guidance to support evidence translation and improvement in complex systems. Conclusion By recognising how agency, interconnectedness and unpredictability influences evidence translation in complex systems, SHIFT-Evidence provides a tool to guide practice and research. The ‘simple rules’ have potential to provide a common platform for academics, practitioners, patients and policymakers to collaborate when intervening to achieve improvements in healthcare

A methodology for understanding the speed of internationalization process: The role of individual-level characteristics

This article presents a methodology that aims to explore how the decision-makers’ cognition affects the speed of internationalization. Managerial cognition is evaluated through the lens of the dual-process theory, which assumes that human information processing is made by two systems: (1) the intuitive and experiential cognitive system – System X, and (2) the rational and analytical cognitive system – System C. The speed of internationalization process is examined in terms of earliness (how soon after inception a firm enters in international markets) and post-internationalization speed (how fast a firm involves with new foreign markets after the first entry). This methodology has been put in practice in a multiple case study: the Portuguese footwear industry. The presence of some misalignments between our initial proposal and its implementation helped us to reshape and emphasize specific processes and behaviors associated to the methodology. The results suggest that, when making the decision about the first international entry, the decision-makers of the firms internationalizing earlier mostly relied on the intuitive cognitive system, while the decision-makers of the firm delaying the first entry showed a predominance of the rational cognitive system. However, regardless of being an early or later entrant, the sampled firms combine intuition with analysis to make the final decision about further involvements with foreign markets, resulting in a gradual and slower post-internationalization. Our in depth-analysis suggests that the Uppsala model could be questioned because the speed of internationalization process seems to be governed by how decision-makers perceive a given reality based on its cognition.N/

Quantitative Measures of Regret and Trust in Human-Robot Collaboration Systems

Human-robot collaboration (HRC) systems integrate the strengths of both humans and robots to improve the joint system performance. In this thesis, we focus on social human-robot interaction (sHRI) factors and in particular regret and trust. Humans experience regret during decision-making under uncertainty when they feel that a better result could be obtained if chosen differently. A framework to quantitatively measure regret is proposed in this thesis. We embed quantitative regret analysis into Bayesian sequential decision-making (BSD) algorithms for HRC shared vision tasks in both domain search and assembly tasks. The BSD method has been used for robot decision-making tasks, which however is proved to be very different from human decision-making patterns. Instead, regret theory qualitatively models human\u27s rational decision-making behaviors under uncertainty. Moreover, it has been shown that joint performance of a team will improve if all members share the same decision-making logic. Trust plays a critical role in determining the level of a human\u27s acceptance and hence utilization of a robot. A dynamic network based trust model combing the time series trust model is first implemented in a multi-robot motion planning task with a human-in-the-loop. However, in this model, the trust estimates for each robot is independent, which fails to model the correlative trust in multi-robot collaboration. To address this issue, the above model is extended to interdependent multi-robot Dynamic Bayesian Networks

Circuit theory of finance and the role of incentives in financial sector reform

The author analyzes the financial system's role in economic growth and stability, addressing several core policy issues associated with financial sector reform in emerging economies. He studies finance's role in the context of a circuit model, with interacting rational, forward-looking, heterogeneous agents. He shows finance to essentially complement the price system in coordinating decentralized intertemporal resource allocation choices made by agents operating with limited information and incomplete trust. He discusses the links between finance and incentives for efficiency and stability in the context of the circuit model. He also identifies incentives and incentive-compatible institutions for reform strategies for financial sectors in emerging economies. Among his conclusions: 1) Circuit theory features important methodological advantages to analyze the role of finance, and to assess structural weaknesses of financial systems under different institutional settings and in different stages of economic development. 2) Incentives for prudence and honesty can protect the stability of the circuit by directing private sector forces unleashed by liberalization. In particular: a) Financial institutions should be encouraged to invest in reputational capital. b) Governments should complement the creation of franchise value by strengthening supervision and by adopting a regulatory regime based on rules designed to align the private incentives of market players with the social goal of financial stability. c) Safety nets to reduce systemic risk should minimize the moral hazard from stakeholders by limiting risk protection and by making the cost of protection sensitive to the risk taken. d) Governments should encourage self-policing in the financial sector. e) Where information and trust are scarce, there is a potential market for them, and governments can greatly improve incentives for optimal provision of information. f) Governments should strengthen the complementarity between the formal and the informal financial sectors. Emphasizing incentives is not to deny the importance of good rules, capable regulators andsupervisors, and strong enforcement measures. It is to suggest that the returns on investments to set up rules, institutions, and enforcement mechanisms can be greater if market players have an incentive to align their own objectives with the social goal of financial stability.Banks&Banking Reform,Economic Theory&Research,Payment Systems&Infrastructure,Environmental Economics&Policies,Financial Intermediation,Economic Theory&Research,Environmental Economics&Policies,Banks&Banking Reform,Financial Intermediation,International Terrorism&Counterterrorism