Theoretical, Measured and Subjective Responsibility in Aided Decision Making

When humans interact with intelligent systems, their causal responsibility for outcomes becomes equivocal. We analyze the descriptive abilities of a newly developed responsibility quantification model (ResQu) to predict actual human responsibility and perceptions of responsibility in the interaction with intelligent systems. In two laboratory experiments, participants performed a classification task. They were aided by classification systems with different capabilities. We compared the predicted theoretical responsibility values to the actual measured responsibility participants took on and to their subjective rankings of responsibility. The model predictions were strongly correlated with both measured and subjective responsibility. A bias existed only when participants with poor classification capabilities relied less-than-optimally on a system that had superior classification capabilities and assumed higher-than-optimal responsibility. The study implies that when humans interact with advanced intelligent systems, with capabilities that greatly exceed their own, their comparative causal responsibility will be small, even if formally the human is assigned major roles. Simply putting a human into the loop does not assure that the human will meaningfully contribute to the outcomes. The results demonstrate the descriptive value of the ResQu model to predict behavior and perceptions of responsibility by considering the characteristics of the human, the intelligent system, the environment and some systematic behavioral biases. The ResQu model is a new quantitative method that can be used in system design and can guide policy and legal decisions regarding human responsibility in events involving intelligent systems

The Responsibility Quantification (ResQu) Model of Human Interaction with Automation

Intelligent systems and advanced automation are involved in information collection and evaluation, in decision-making and in the implementation of chosen actions. In such systems, human responsibility becomes equivocal. Understanding human casual responsibility is particularly important when intelligent autonomous systems can harm people, as with autonomous vehicles or, most notably, with autonomous weapon systems (AWS). Using Information Theory, we develop a responsibility quantification (ResQu) model of human involvement in intelligent automated systems and demonstrate its applications on decisions regarding AWS. The analysis reveals that human comparative responsibility to outcomes is often low, even when major functions are allocated to the human. Thus, broadly stated policies of keeping humans in the loop and having meaningful human control are misleading and cannot truly direct decisions on how to involve humans in intelligent systems and advanced automation. The current model is an initial step in the complex goal to create a comprehensive responsibility model, that will enable quantification of human causal responsibility. It assumes stationarity, full knowledge regarding the characteristic of the human and automation and ignores temporal aspects. Despite these limitations, it can aid in the analysis of systems designs alternatives and policy decisions regarding human responsibility in intelligent systems and advanced automation

Individual-Level Determinants of the Propensity to Shirk

Employee shirking, where workers give less than full effort on the job, has typically been investigated as a construct subject to group and organization-level influences. Neglected are individual differences that might explain why individuals in the same organization or work-group might shirk. The present study sought to address these limitations by investigating subjective well-being (a dispositional construct), job satisfaction, as well as other individual-level determinants of shirking behavior. Results identified several individual-level determinants of shirking. Implications of the results are discussed

Implementing Inflation Targeting in Brazil

Brazil has put in place an inflation-targeting framework for monetary policy in mid-1999, less than six months after moving to a floating exchange rate system. This paper presents the macroeconomic background that has led to the shift in monetary policy regime, and describes the general institutional arrangements and operational framework that has been adopted. The paper also discusses the basic modeling approach that has aided the decision-making process in the initial phase of inflation targeting in Brazil. We describe the family of small-scale macroeconomic models that has been used for informing and disciplining discussions about monetary policy within the Central Bank. These models contain few equations and few variables, but carry a considerable theoretical content and provide a stylized representation of the monetary policy transmission mechanism. They are easily understood, and especially suitable for simulation of a wide range of issues. We conclude with the main lessons that may be drawn from the initial Brazilian experience with inflation targeting.

Environmental Education in the Public Sphere: Comparing Practice with Psychosocial Determinants of Behavior and Societal Change

Environmental education of the general public is widely practiced by a variety of types of organizations. Dedicated environmental groups, nature centers, zoos, parks, and other entities work on issues ranging from local threats to air, water, and habitat to global problems such as climate change and deforestation. A great deal of those efforts focus largely on providing information and raising awareness. Behavioral research and change models, however, suggest other factors are important in order to effect change on an individual, regional, or societal level. An analysis of environmental education in practice, examining methods and materials in use, showed the degree to which there were alignments between the content and psychosocial determinants of change, as well as how actions related to change theories. This mixed-methods study of groups doing environmental education in the public sphere compared their practices with the factors shown to help predict pro-environmental behavior, why people change their actions and habits. Through this survey research and multiple case study, increased knowledge and understanding can help inform future efforts at change on critical local, national, and world environmental problems. It can also lead to further research into environmental education, using behavior and change theories

Adaptive Allocation of Decision Making Responsibility Between Human and Computer in Multi-Task Situations

A unified formulation of computer-aided, multi-task, decision making is presented. Strategy for the allocation of decision making responsibility between human and computer is developed. The plans of a flight management systems are studied. A model based on the queueing theory was implemented

An Epistemology for Agribusiness: Peers, Methods and Engagement in the Agri-Food Bio System

The IFAMR is published by the International Food and Agribusiness Management Association www.ifama.orgagribusiness, epistemology, research methods, wicked problems, engaged scholarship, research rigor, grounded theory, Agribusiness, Research and Development/Tech Change/Emerging Technologies, Teaching/Communication/Extension/Profession, Q130,