An Analysis Capability for System of Systems Research

2009 ◽  
Vol 53 (12) ◽  
pp. 814-818
Author(s):  
Elizabeth K. Bowman ◽  
Jeffrey A. Smith
Keyword(s):  
Decision Making ◽  
Human Behavior ◽  
Complex Problem ◽  
System Of Systems ◽  
Systems Of Systems ◽  
Analysis Process ◽  
Systems Research ◽  
Utility Of Information ◽  
Human Decision ◽  
Definition Of

This paper proposes an analysis capability for systems of systems research in military settings. A new approach is needed due to the increasingly complex socio-technical nature of Command and Control (C2). This research seeks to advance the Army analysis process by developing a capability to examine cognitive, social and technical aspects of information sharing and consequential decision making requirement for C2. We first review the definition of system of systems. Next, we establish the agent-based modeling and simulation (ABMS) paradigm as a useful method for analysis because of its facility for exploring large and complex problem spaces. This is followed by some structural issues addressed by ABMS with an emphasis on the challenge of representing human behavior in psychologically plausible ways. We then present one instantiation of ABMS that incorporates a representation of human decision making and the utility of information in a small vignette. We consider the suitability of this ABMS for system of system analyses with respect to how the decision making processes represent human decision making behavior. Finally, we discuss an ongoing approach to improve human behavior representations in the agents of this ABMS.

InDrone: a 2D-based drone flight behavior visualization platform for indoor building inspection

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ricardo Eiris ◽  
Gilles Albeaino ◽  
Masoud Gheisari ◽  
William Benda ◽  
Randi Faris
Keyword(s):  
Decision Making ◽  
Experimental Methodology ◽  
Flight Behavior ◽  
Temporal Data ◽  
Flight Pattern ◽  
Content Type ◽  
Pilot Data ◽  
Flight Operations ◽  
Human Decision ◽  
Definition Of

PurposeThe purpose of this research is to explore how to visually represent human decision-making processes during the performance of indoor building inspection flight operations using drones.Design/methodology/approachData from expert pilots were collected using a virtual reality drone flight simulator. The expert pilot data were studied to inform the development of an interactive 2D representation of drone flight spatial and temporal data – InDrone. Within the InDrone platform, expert pilot data were visually encoded to characterize key pilot behaviors in terms of pilots' approaches to view and difficulties encountered while detecting the inspection markers. The InDrone platform was evaluated using a user-center experimental methodology focusing on two metrics: (1) how novice pilots understood the flight approaches and difficulties contained within InDrone and (2) the perceived usability of the InDrone platform.FindingsThe results of the study indicated that novice pilots recognized inspection markers and difficult-to-inspect building areas in 63% (STD = 48%) and 75% (STD = 35%) of the time on average, respectively. Overall, the usability of InDrone presented high scores as demonstrated by the novice pilots during the flight pattern recognition tasks with a mean score of 77% (STD = 15%).Originality/valueThis research contributes to the definition of visual affordances that support the communication of human decision-making during drone indoor building inspection flight operations. The developed InDrone platform highlights the necessity of defining visual affordances to explore drone flight spatial and temporal data for indoor building inspections.

From Human Decision Making to DMSS Architecture

2011 ◽  
pp. 40-70 ◽  
Author(s):  
Jean-Charles Pomerol ◽  
Frederic Adam
Keyword(s):  
Decision Making ◽  
Heuristic Search ◽  
Decision Process ◽  
Decision Support Tools ◽  
Human Decision Process ◽  
Look Ahead ◽  
Support Tools ◽  
Cognitive Levels ◽  
Human Decision ◽  
Definition Of

In this chapter we begin by featuring the main characteristics of the human decision process. Then, from these traits, we separate the decision process between diagnosis and look-ahead. We explain why DMSSs are mainly look-ahead machines. We claim that look-ahead is generally performed via heuristic search and “what-if analysis” at different cognitive levels. This leads to a functional definition of DMSSs and to different architectures adapted to heuristic search and moreover, paves the way for an analysis of the decision support tools.

scholarly journals Approach to Financial Decision Making within Quantum Physics and Neurosciences

2020 ◽  
Vol 3 (2) ◽  
pp. 1
Author(s):  
Ana Njegovanović
Keyword(s):  
Decision Making ◽  
Quantum Mechanics ◽  
Human Behavior ◽  
Quantum Physics ◽  
Posterior Parietal Cortex ◽  
Complex Problem ◽  
Good Prediction ◽  
Financial Decision Making ◽  
Functional Relationships ◽  
Financial Decision

This paper focuses on the study of the functional relationships between the tools of neuroscience, neurofinance and psychology on the one hand, and quantum physics / quantum mechanics and neurophysiology on the other. Can physics / quantum mechanics help explain / understand human behavior through the Shrödinger cat platform (perhaps we can explain the most mysterious phenomena: human behavior - cerebral secretion?)? The concepts of quantum mechanics allow a good prediction of human decision making within Schrödinger's cat (two particles can talk to each other even at a distance of a galaxy, perhaps in this sense can help explain an extremely complex decision making system), and define the "connection of quantum models with neurophysiological processes in the brain “... which is a very complex problem.” (Haven and Khrennikov) The application of quantum physics and neuroscience in finance allows us to consider the complexity of financial decision making, while the connection between quantum physics and psychology manifests itself as the field of quantum physics seeks to understand the fundamental nature of particles. while the field of psychology seeks to explain human nature along with its inherent misconceptions.If decision-making is a process of gathering evidence in favor of different alternatives over time, the process is discontinued once the decision limit is reached, followed by choice of decision. e activity within the posterior parietal cortex several important questions remain unanswered. Neural mechanisms that support the accumulation of evidence record the activities of individual neurons in different parts of the prefrontal cortex (PFC) and the lateral intraparietal area (LIP).

Editors’ Introduction

2017 ◽  
Author(s):  
Wim Bernasco ◽  
Henk Elffers ◽  
Jean-Louis van Gelder
Keyword(s):  
Decision Making ◽  
Law Enforcement ◽  
Empirical Evidence ◽  
Research Methods ◽  
Human Behavior ◽  
Human Decision ◽  
Criminal Conduct

Decision making is central to all human behavior, including criminal conduct. Virtually every discussion about crime or law enforcement is guided by beliefs about how people make decisions in one way or another. This interdisciplinary handbook integrates insights about the role of human decision making as it relates to crime. It contains reviews of the main theories of offender decision making and also reviews of empirical evidence on topics as diverse as desistance, crime locations, co-offending, victimization, and criminal methods and tools. It further includes in-depth treatments of the principal research methods for studying offender decision making and a series of chapters on specific types of crime.

scholarly journals Evaluating multi-purpose syndromic surveillance systems – a complex problem

2021 ◽  
Vol 13 (3) ◽  
Author(s):  
Roger Morbey ◽  
Gillian Smith ◽  
Isabel Oliver ◽  
Obaghe Edeghere ◽  
Iain Lake ◽  
...  
Keyword(s):  
Decision Making ◽  
Syndromic Surveillance ◽  
Detection Algorithm ◽  
Complex Problem ◽  
Automated Detection ◽  
Surveillance Systems ◽  
Practical Application ◽  
Wide Range ◽  
Human Decision ◽  
Detection Capabilities

Surveillance systems need to be evaluated to understand what the system can or cannot detect. The measures commonly used to quantify detection capabilities are sensitivity, positive predictive value and timeliness. However, the practical application of these measures to multi-purpose syndromic surveillance services is complex. Specifically, it is very difficult to link definitive lists of what the service is intended to detect and what was detected. First, we discuss issues arising from a multi-purpose system, which is designed to detect a wide range of health threats, and where individual indicators, e.g. ‘fever’, are also multi-purpose. Secondly, we discuss different methods of defining what can be detected, including historical events and simulations. Finally, we consider the additional complexity of evaluating a service which incorporates human decision-making alongside an automated detection algorithm. Understanding the complexities involved in evaluating multi-purpose systems helps design appropriate methods to describe their detection capabilities.

scholarly journals Any way the brain blows? The nature of decision noise in random exploration

2018 ◽  
Author(s):  
Siyu Wang ◽  
Robert C Wilson
Keyword(s):  
Decision Making ◽  
Cognitive Control ◽  
Recent Work ◽  
Human Behavior ◽  
External Noise ◽  
Behavioral Variability ◽  
The World ◽  
Human Decision ◽  
Adaptive Noise ◽  
The Brain

Human decision making is inherently variable. While this variability is often seen as a sign of suboptimality in human behavior, recent work suggests that randomness can actually be adaptive. An example arises when we must choose between exploring unknown options or exploiting options we know well. A little randomness in these `explore-exploit' decisions is remarkably effective as it encourages us to explore options we might otherwise ignore. Moreover, people actually use such `random exploration' in practice, increasing their behavioral variability when it is more valuable to explore. Despite this progress, the nature of adaptive `decision noise' for exploration is unknown -- specifically whether it is generated internally, from stochastic processes in the brain, or externally, from stochastic stimuli in the world. Here we show that, while both internal and external noise drive variability in behavior, the noise driving random exploration is predominantly internal. This suggests that random exploration depends on adaptive noise processes in the brain which are subject to cognitive control.

scholarly journals Deep exploration as a unifying account of explore-exploit behavior

2020 ◽  
Author(s):  
Robert C Wilson ◽  
Siyu Wang ◽  
Hashem Sadeghiyeh ◽  
Jonathan D. Cohen
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Human Behavior ◽  
Information Seeking ◽  
The Other ◽  
Behavioral Variability ◽  
Simple Task ◽  
Stochastic Planning ◽  
Human Decision ◽  
Deep Exploration

Many decisions involve a choice between exploring unknown opportunities and exploiting well-known options. Work across a variety of domains, from animal foraging to human decision making, has suggested that animals solve such ``explore-exploit dilemmas'' with a mixture of two strategies: one driven by information seeking (directed exploration) and the other by behavioral variability (random exploration). Here we propose a unifying account in which these two strategies emerge from a kind of stochastic planning, known in the machine learning literature as Deep Exploration. In this model, the explore-exploit decision is made by stochastic simulation of plausible futures that are deep, in that they extend far into the future, and narrow, in that the number of possible futures they consider is small. By applying Deep Exploration to a simple explore-exploit task we show theoretically how directed and random exploration can emerge in these settings. Moreover, we show that Deep Exploration implies a tradeoff between directed and random exploration that is mediated by the number of simulations, or samples --- with more samples leading to increased directed exploration and decreased random exploration at the expense of greater time taken to respond. By measuring human behavior on the same simple task, we show that this reaction-time-mediated tradeoff exists in human behavior both between and within participants. We therefore suggest that Deep Exploration is a unifying account of explore-exploit behavior in humans.

scholarly journals Task-independent metrics of computational hardness predict performance of human problem-solving

2021 ◽  
Author(s):  
Juan Pablo Franco ◽  
Karlo Doroc ◽  
Nitin Yadav ◽  
Peter Bossaerts ◽  
Carsten Murawski
Keyword(s):  
Decision Making ◽  
Computational Complexity ◽  
Problem Solving ◽  
Complexity Theory ◽  
Human Behavior ◽  
Cognitive Tasks ◽  
Complex Tasks ◽  
Effort Allocation ◽  
Human Decision ◽  
Human Problem

The survival of human organisms depends on our ability to solve complex tasks, which is bounded by our limited cognitive capacities. However, little is known about the factors that drive complexity of the tasks humans face and their effect on human decision-making. Here, using insights from computational complexity theory, we quantify computational hardness using a set of task-independent metrics related to the computational requirements of individual instances of a task. We then examine the relation between those metrics and human behavior and find that these metrics predict both performance and effort allocation in three canonical cognitive tasks in a similar way. Our findings demonstrate that the ability to solve complex tasks can be predicted from generic metrics of their inherent computational hardness.

Evaluating Human Decision-Making Theories With Monkey Data

2013 ◽  
Author(s):  
Scott D. Brown ◽  
Pete Cassey ◽  
Andrew Heathcote ◽  
Roger Ratcliff
Keyword(s):  
Decision Making ◽  
Human Decision
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