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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. 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.

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Computer Aided Decision Support in Telecommunications

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. 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. 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.

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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.

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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.

However, there does not yet exist either an adequate and detailed understanding of how such modeling can be done, nor a complete model of how the brain interacts with complex regulatory and monitoring systems throughout the body. These and other difficulties make it highly unlikely that in the next two decades anyone could build a neurophysiologically plausible model of the whole brain and its array of specialized and general-purpose higher cognitive functions. Statistical rigor is necessary to justify the inferential leap from data to knowledge, and many difficulties arise in attempting to bring statistical principles to bear on massive data.

Among these hurdles are sampling bias, provenance, and control of error rates. All statistical methods rely on assumptions about how the data were gathered and sampled; however, massive datasets are often constructed from many subcollections of data, each of which was amassed using a different sampling scheme for a different purpose. The analyst may have little control or insight into this collection.

Finding 3. While improved information availability can improve the quality of decision making, more information alone is not sufficient. This is particularly evident in complex scenarios where the goals of different team members are not completely aligned and delays make it difficult to attribute effects to actions. Although existing statistical tools can address these issues, much work remains to be done in developing and applying them to massive data. In particular, there is still a gap: the middleware that would enable statistical tools to interact with distributed systems.

The Committee on the Analysis of Massive Data identified several key research areas:. Such computers could be programmed to perform well-understood, limited, and often repetitious tasks. They could display almost real-time radar returns, hold and represent text for an author, and list inventory. In these situations, the automation executes minor, even incidental, tasks that support human decision making. 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 surveillance camera recordings. Advances in computing capabilities over recent decades now make it reasonable to consider how to integrally incorporate automation into complex decision-making systems.

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This progress has enabled human beings and computers to assemble into networks composed of geographically dispersed members. As computing devices have gained increasing abilities to intelligently interpret information and to act over long periods of time with diminished human supervision, their ability to act as teammates rather than simply be tools has increased. Although the distinction between tool and teammate is not a sharp one, the difference in experience working with a device that is a teammate rather than simply a tool is powerful.

For instance, consider two systems that might help a person in writing a paper. The tool-system allows the person to easily reach Google and search for citations. The teammate-system goes farther and provides some functions akin to what a colleague could bring to the partnership. When the user is ready, the teammate-system presents the results of its searches, and perhaps some analyses; when the user has selected in this case references to include in the paper being drafted, the system can do all formatting required so that text is ready to drop into place.

Categorizing an automated element as a tool or a teammate does not carry great importance, except to recognize that the relationship between humans and computers is changing. The capabilities described in the last paragraph may look primitive before too many years have elapsed.