Complex Operational Decision Making in Networked Systems of Humans and Machines A Multidisciplinary Approach (2014) / Chapter Skim
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Chapter 2 Computing and Decision Making Today
Chapter 2 Computing and Decision Making Today
Pages 11-22
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From page 11... ...
But with today's information technology, the amount of information that can be assembled and the number of options that can be examined have grown tremendously. As an operation unfolds, it will build on reports and forecasts about weather, tide, wind, and storm conditions, movements of others on transportation routes, aerial images and other sensed data, human-generated reports from the field, media and intelligence reports, information traffic over social media, data exhaust , and so on.
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From page 12... ...
The response to the 2013 Boston Marathon bombing is another case, one that may be more akin to some military decision making. In addition to multiple, partial information, the decision makers who managed the immediate response had to incorporate preliminary forensic evidence, crowd sourced inputs (of untested value)
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From page 13... ...
As a result, although widely referred to in operational situations and for training, it is not a common framework in the research community. Recently the military literature has addressed the inadequacy of the OODA loop to deal with complex situations where the decision maker does not have access to a model of the underlying mechanisms between actions and outcomes (Benson and Rotkoff, 2011)
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From page 14... ...
. While those steps are inherent to any careful decision making, for complex decisions the OODA loop framework does not readily reflect feedback loops between the steps and branching to consider multiple choices of action, both of which are common.
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From page 15... ...
For example, social networking was responsible for many false claims just after the Boston Marathon bombing in April 2013, and subsequently, throughout the hunt for the perpetrators. 2 In addition to meeting the challenge of supporting its intended user, systems that incorporate data analysis can encounter situations in which hostile entities intend to deceive the decision maker.
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From page 16... ...
Analogous technology that uses big data to understand human networks and interactions is also affecting other important decisions such as where to distribute malaria nets in Africa, where to send emergency teams in a disaster, how to advertise a political candidate, and how to induce people to contribute to charity. Culture should also be considered, because it affects teammembers' attitudes and unspoken assumptions, such as how they feel about privacy, trust, sharing, and so on.
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From page 17... ...
This trend is powered by the confluence of several technical and societal trends that are projected to accelerate over the coming years: the exploding volume and variety of data, the accelerating use of the Internet to share these data and to support team decision making, and the widespread adoption of personal mobile devices that give individuals nearly continuous opportunities to communicate, to collect data about themselves and their surroundings, and to access online computer assistance. Analyses of massive datasets have already led to breakthroughs in fields as diverse as genomics, astronomy, health care, urban planning, and marketing.
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From page 18... ...
A 2008 report from the National Academies (Emerging Cognitive Neuroscience and Related Technologies) observed "The global scientific computing community is approaching an era in which high-end computing will, in principle, be sufficient in capacity and computational power to model the human brain.
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From page 19... ...
The Committee on the Analysis of Massive Data (2013) identified several key research areas: Data representation, including characterizations of the raw data and transformations that are often applied to data, particularly transformations that attempt to reduce the representational complexity of the data; Computational complexity issues and how the understanding of such issues supports characterization of the computational resources needed and of trade-offs among resources; Statistical model building in the massive data setting, including data cleansing and validation; Sampling, both as part of the data-gathering process and as a key methodology for data reduction; and Methods for including people in the data-analysis loop.7 Much of the current report focuses on the final research area listed above.
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From page 20... ...
The results provided are useful, but the computers are not centrally involved in determining how the decision process is orchestrated over time. Humans have tended to delegate discrete tasks to computation, such as searching for information in a data base, mining large volumes of data, depicting information in visual form that is more amenable to human understanding, and monitoring some behavior (such as streams of credit card transactions or surveilla nce camera recordings)
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From page 21... ...
In the future, each human or machine participant might: Proffer information or observations or suggestions to team members that advance some aspect of the shared objectives; Proffer critiques of the team's problem-solving strategies; Possess "self-awareness" when approaching overload and recruit help in such a situation; Monitor teammates' problem-solving process and execution, and then anticipate the information needs of others; give and accept feedback; identify gaps in approach; and cover for another's execution failure; Explain how a result was reached; and Adjust activities over time to account for changing needs of the team and its members; adapt as the decision scenario unfolds. This point of view that sees human-machine decision making as a collaboration echoes the 2012 Defense Science Board report The Role of Autonomy in DoD Systems: The Task Force reviewed many of the DoD-funded studies on "levels of autonomy" and concluded that they are not particularly helpful to the autonomy design process.
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From page 22... ...
To fully exploit this situation, engineers can use a growing number of design techniques for building and structuring human-machine decision-making teams. The committee analyzed multiple aspects of the machine-human relationship.
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