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A red team could identify ways in which observations might be made misleading, decisions anticipated, and actions countered, thus undercutting the applicability of an OODA-loop description of the decision-making process. In addition, the implied sequential nature of the OODA loop—even if there is feedback between stages and perhaps multiple trips through the loop—does not fit well with real, complex decision making. In responding to a natural disaster, for example, decision making is extremely interactive, which is not modeled well with an OODA framework.

Both of these exercises. The committee believes that the OODA-loop construct is not well matched to complex decision making with large volumes of information; while the four stages are part of any decision-making process, they can be combined in multiple ways. Consequently, it developed the following finding:.

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Finding 1. A common representation of the decision-making process, used to train fighter pilots in rapid decision-making for air combat, calls for sequential steps to observe, update beliefs, choose an action, and take the action the so-called OODA loop. 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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The study of decision making in complex situations, and the design of automated decision support systems, requires an understanding of those complexities. Thus the OODA-loop framework may not be sufficient in these contexts.

Components of Decision Support Systems (DSS)

Early decision-making models tended to assume that decision making occurred at the conscious level of processing. A more nuanced view was presented by the theorist, J. Rasmussen , who divided the decision-making process of skilled operators into three categories: Skill-, Rule-, and Knowledge-based procedures.

Skill-based behavior refers to those capabilities that are sensory-motor and developed after a period of training, such as riding a bike. Rule-based behavior refers to those that are based on learned rules or procedures, such as following a recipe. In this taxonomy, knowledge-based processing is the highest level of cognitive control because it includes the challenge of solving novel problems Cummings, For example, recent evidence about human thought implies that decisions by experts are often reached subconsciously, with reason and logic coming afterward to justify the decision Mercier and Sperber, Recognition-primed decision making Klein, involves rapid pattern matching to the situation, one of the powerful properties of fast, subconscious systems.

Heuristics and rule-following present a mix of behavior at the conscious and subconscious levels of processing. At the subconscious level, researchers have identified numerous heuristics that people use to simplify and speed up decision making—effectively, pattern matching to situations previously experienced. This decision making is often referred to as fast and frugal Gigerenzer and Goldstein, The past decade has seen significant progress in developing technologies and methods that support human sense-making and decision-making processes in complex domains. Understanding the dynamics of a complex system or organization can help one foresee the side effects of a decision or anticipate events before they occur.

Many studies have been undertaken on measuring and supporting situation awareness, especially for individual decision makers, but there are still major gaps in our understanding of how to design and evaluate technologies and methods to provide effective cognitive support for individual and team sense making Klein et al. Traditional models of human decision making focus entirely upon mental processing—all the action takes place in the brain—but another important trend in our understanding of human behavior is to understand the role of embodiment—that the human body exists in the world, interacting with it in ways that enhance our ability to function.

Norman described it as a melding of knowledge in the head and knowledge in the world, because when accomplishing some task, the environment provides much of the information required as well as providing constraints, guides, and suggested courses of action Norman, , Information systems can be designed to support the human decision maker in tasks or subtasks that are domain or situation specific. More and more systems are able to respond intelligently to queries in natural language e. At the same time, some of these technologies have also complicated the decision-making process.

For example, social networking was responsible for many false claims just after the Boston Marathon bombing in April , and subsequently, throughout the hunt for the perpetrators. 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. When such strategic actors are present, they might play a meta-level role in determining what we are able to observe. These potential vulnerabilities must be considered when designing and using information systems as decision aids.

For example, our actions, including further information gathering, can inform adversaries about our current state of knowledge. Last accessed March 19, A decision-making context and process can be characterized along dimensions such as the following:. The list above characterizes many aspects of the human decision-making process, and each dimension will influence an information system that is designed to support decision making.

Any or all of these dimensions might be considered when developing such an information system. Many other factors have crucial effects on decisionmaking, such as emotions, social context, relationships, organizational structures, authority systems, and so forth. And the way individuals work in networks can have strong impacts. Ignoring these factors can lead to failures of team decision making, and an understanding of these factors must inform the design and incorporation of technologies. Currently, tools that assist with team coordination are making great advances.

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. There is a good deal of emerging research on this topic. More and more, these data are considered potential sources of knowledge, requiring increasingly sophisticated analysis techniques to uncover their relational and semantic underpinnings.

Arguably, we currently stand at the beginning of a decades-long trend toward increasingly evidence-based, data-informed decision making across all walks of life. 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. Local governments use historical and real-time data feeds to improve decisions about traffic control and about where and when to allocate foot police to keep the peace.

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Individuals now use mobile devices to capture continuous data about the number of steps they take every day, their weight, and other personal health data in an effort to understand and improve their own health. Answers, Quora.

The process of inferring true knowledge from it is non-trivial. The sheer volume of the data requires computing just to prepare and filter the data for human interpretation.

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But that may not be enough, because the filtered output can still be enormous, and current capabilities. Last accessed March 24, The fact that appropriate results are often at the top of the list is an amazing accomplishment, but it is still necessary for a user—an analyst—to assess the top N hits to determine which are most promising.

Humans are remarkably good and fast at this, thus exceeding the capabilities of computers, although even then, humans can be fooled by erroneous information, superficial associations, manipulation of search engines, and other artifacts of the data or the algorithms that filter it. Even if feasible, the timeliness of decision making will then be limited by the speed of a human analyst.

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Finding 2. Increasingly the data used to support computer-assisted decisions are drawn from heterogeneous sources e. Current techniques for filtering and aggregating these disparate data types into a well-characterized input for decision making are limited, which therefore limits the quality of the decisions. Yet it is still often the case today that the human has to adapt to the machine, rather than the other way around. It is important to understand and quantify the unique capabilities of the human and the information system to allow both to function optimally.

It is also critical to recognize that exploiting large bodies of data is not necessarily better than traditional approaches. Smaller amounts of data, including data drawn via a process of sampling from large stores or streams of data, may provide the most important inputs to decision making. As discussed in detail in the National Research Council report Frontiers in Massive Data Analysis , there are still substantial challenges for massive data.

At the subconscious level, researchers have identified numerous heuristics that people use to simplify and speed up decision making—effectively, pattern matching to situations previously experienced. This decision making is often referred to as fast and frugal Gigerenzer and Goldstein, The past decade has seen significant progress in developing technologies and methods that support human sense-making and decision-making processes in complex domains. Understanding the dynamics of a complex system or organization can help one foresee the side effects of a decision or anticipate events before they occur.

Many studies have been undertaken on measuring and supporting situation awareness, especially for individual decision makers, but there are still major gaps in our understanding of how to design and evaluate technologies and methods to provide effective cognitive support for individual and team sense making Klein et al.


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  • Traditional models of human decision making focus entirely upon mental processing—all the action takes place in the brain—but another important trend in our understanding of human behavior is to understand the role of embodiment—that the human body exists in the world, interacting with it in ways that enhance our ability to function. Norman described it as a melding of knowledge in the head and knowledge in the world, because when accomplishing some task, the environment provides much of the information required as well as providing constraints, guides, and suggested courses of action Norman, , Information systems can be designed to support the human decision maker in tasks or subtasks that are domain or situation specific.

    More and more systems are able to respond intelligently to queries in natural language e. At the same time, some of these technologies have also complicated the decision-making process. For example, social networking was responsible for many false claims just after the Boston Marathon bombing in April , and subsequently, throughout the hunt for the perpetrators. 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.

    When such strategic actors are present, they might play a meta-level role in determining what we are able to observe. These potential vulnerabilities must be considered when designing and using information systems as decision aids. For example, our actions, including further information gathering, can inform adversaries about our current state of knowledge.

    Last accessed March 19, A decision-making context and process can be characterized along dimensions such as the following:. The list above characterizes many aspects of the human decision-making process, and each dimension will influence an information system that is designed to support decision making.

    Any or all of these dimensions might be considered when developing such an information system. Many other factors have crucial effects on decisionmaking, such as emotions, social context, relationships, organizational structures, authority systems, and so forth. And the way individuals work in networks can have strong impacts. Ignoring these factors can lead to failures of team decision making, and an understanding of these factors must inform the design and incorporation of technologies. Currently, tools that assist with team coordination are making great advances. 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.

    There is a good deal of emerging research on this topic.