AbstractÐThis paper examines and proposes an approach to writing software specifications, based on research in systems theory, cognitive psychology, and human-machine interaction. The goal is to provide specifications that support human problem solving and the tasks that humans must perform in software development and evolution. A type of specification, called intent specifications, is constructed upon this underlying foundation. Index TermsÐRequirements, requirements specification, safety-critical software, software evolution, human-centered specifications, means-ends hierarchy, cognitive engineering.

Fund. Nelson Repenning graciously permitted us to administer the tasks in his introductory system dynamics class. We also thank Jim Doyle, Michael Radzicki, Terry Tivnan the referees for helpful comments. Christopher Hunter assisted with data entry.

With the advent of computers in the experimental labs, dynamic systems have become a new tool for research on problem solving and decision making. A short review of this research is given and the main features of these systems (connectivity and dynamics) are illustrated. To allow systematic approaches to the influential variables in this area, two formal frameworks (linear structural equations and finite state automata) are presented. Besides the formal background, the article sets out how the task demands of system identification and system control can be realised in these environments, and how psychometrically acceptable dependent variables can be derived. The use of computer-simulated scenarios in problem-solving research has become increasingly popular during the last 25 years (for a representative collection of papers see, e.g., the two editions from Sternberg & Frensch, 1991, and Frensch & Funke, 1995). This new approach to problem solving seems attractive for several reasons. In contrast to static problems, computer-simulated scenarios provide a unique opportunity to study human problem-solving and decision-making behaviour when the task environment and subjects ’ actions change concurrently. Subjects can manipulate a specific scenario via a number of input variables (typically ranging from 2 to 20, and in some exceptional instances even up to 2000), and they observe the system’s state changes in a number of output variables. In exploring and/or controlling a system, subjects have to continuously acquire and use knowledge about the internal structure of the system.

This section reviews a specialty within the field of decision-making known as dynamic decision-making. Dynamic decisions are characterized by a decision-maker choosing among various actions at different points in time in order to control and optimize performance of a dynamic stochastic system. Realistic examples include fighting fires, navigational control, battlefield decisions, medical emergencies, and so on. The section has four parts: The first reviews basic theory concerning optimal decision principles in a dynamic context; the second summarizes empirical approaches to the study of human performance on dynamic decision tasks; the third presents theoretical models that describe how humans learn to control dynamic systems; and the last discusses methodological issues arising from the study of complex decisions including differences between field versus laboratory research.

The bullwhip effect describes the tendency for the variance of orders in supply chains to increase as one moves upstream from consumer demand. Previous research attributes this phenomenon to both operational and behavioral causes. Operational causes are features of the institutional setting that lead rational agents to amplify changes in demand, while behavioral causes arise from suboptimal decision-making. This paper examines causes of the bullwhip through experiments with a serial supply chain, using the Beer Distribution Game. Unlike prior studies, we control all four commonly cited operational causes of the bullwhip, including uncertainty about customer demand. We eliminate demand uncertainty completely by making customer demand constant and known to all participants. Despite these controls, order amplification, instability, and supply line underweighting remain pervasive. We propose a new behavioral cause of the bullwhip, coordination risk, that arises when players place excessive orders to address the perceived risk that others will not behave optimally. We test two strategies to mitigate coordination risk: (1) holding additional on-hand inventory, and (2) creating common knowledge by informing participants of the optimal policy. Both strategies reduce, but not eliminate, the bullwhip effect. Holding excess inventory reduces order amplification by providing a buffer against the

Routinely, so much well-intentioned effort is thwarted and morale is destroyed in organisations because of the lack of commitment to decisions. The likelihood and consequence of this is greatest where divergent groups, ie, different divisions, department or units are involved. This paper discusses a Group Model Building case study using qualitative system dynamics to create consensus, team learning and shared vision in a public organisation. The case involves determining planning priorities for a division of the Ministry of Health in New Zealand, leading to the creation of a business plan. The methodology involves a three-step process starting with structured brainstorming using the partial KJ (Jiro Kawakita) technique to identify priority areas and clustering them into 'affinity ' groups. Next, the priority clusters are condensed into 'variables ' and used by the participants to construct causal loop diagrams representing 'systems of priorities ' (in contrast to list of priorities). Finally, through a group process, leverage points or key priorities are identified and translated into a business plan. Strong group resistance was encountered at this stage when attempting to reduce the number of priority areas as suggested by the traditional priority matrix technique. Systems thinking approach was used to alleviate this problem. The approach offers significant promise in using qualitative system dynamics with nonsystems experts. The methodology can be applied to any change management initiative and complex decisions such as restructuring, reengineering, and supply chain design. The expected outcomes are greater commitment and shared vision.

The aim of this project was to investigate whether the military aerospace safety process could be improved with respect to the analysis of human failures. This project considered the potential for reducing risk at the man/machine interface by extending current procedures to incorporate existing human factors techniques. The study also considered whether human factors methods and safety analysis techniques could be adapted to analyse system induced human failures. Areas of weakness identified by the literature survey provided a focus for the proposed solution. The process developments were demonstrated by performing an evaluation on a fictitious case study, the results were evaluated to assess whether the extended process provided any significant benefits.

Optimization to measure performance in

www.elsevier.com/locate/cogsys The challenge of complexity for cognitive systems

Beyond IQ: A latent state-trait analysis of general intelligence, dynamic