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The Reinvention of Decision Making (Orasanu, Connolly, 1993)

A Proposed Heuristic for a Computer Chess Program (John L. Jerz)
Problem Solving and the Gathering of Diagnostic Information (John L. Jerz)
A Concept of Strategy (John L. Jerz)
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The Case for Using Probabilistic Knowledge in a Computer Chess Program (John L. Jerz)
Resilience in Man and Machine


In: Klein, Orasanu, Calderwood, Zsambok, Decision Making in Action: Models and Methods
Orasanu and Connolly describe their concept of naturalistic decision making, which is performed by experts in a larger context, and often involving strings of decisions, actions, evaluations, and contemplation. Classical decision making had previously focused on a single event, in a closed environment, and performed by unmotivated non-experts. Orasanu and Connolly contend that all decisions are essentially part of a larger context of goal seeking behavior, and must be seen as part of a process that involves values, recognition, sensing, knowledge and expertise.
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p.6 Decisions are embedded in task cycles that consist of defining what the problem is, understanding what a reasonable solution would look like, taking action to reach that goal, and evaluating the effects of that action. As Brehmer (1990) states in describing his research on dynamic decision making,
The study of decision making in a dynamic, real time context, relocates the study of decision making and makes it part of the study of action, rather than the study of choice. The problem of decision making, as seen in this framework, is a matter of directing and maintaining the continuous flow of behavior towards some set of goals rather than as a set of discrete episodes involving choice dilemmas. (p. 26)
p.7 A fundamental contention of this volume is that decision performance in everyday situations is a joint function of two factors: (1) features of the task, and (2) the subject's knowledge and experience relevant to that task.
p.7-9 Eight important factors characterize decision making in naturalistic settings, but frequently are ignored in decision-making research. ...
1. Ill-structured problems: Real decision problems rarely present themselves in the neat, complete form the event model suggests. The decision maker will generally have to do significant work to generate hypotheses about what is happening, to develop options that might be appropriate responses. ... Observable features of the setting may be related to one another by causal links, interactions between causes, feedback loops, and so on. ...
2. Uncertain dynamic environments: Naturalistic decision making typically takes place in a world of incomplete and imperfect information. The decision maker has information about some part of the problem... but not about others... Information may be ambiguous or simply of poor quality ... diagnostic tests [give indeterminate results] ...
3. Shifting, ill-defined, or competing goals: ... We expect the decision maker to be driven by multiple purposes, not all of them clear, some of which will be opposed to others ... These conflicts and tradeoffs may arise in NDM [naturalistic decision making] because they are often novel and must be resolved swiftly, and because the situation may change quickly, bringing new values to the fore... Often, larger goals will provide direction, since decisions typically are embedded in broader tasks. ...
4. Action/feedback loops: ... In NDM ... it is much more common to find an entire series of events, a string of actions over time that are intended to deal with the problem, or to find out more about it, or both ... early mistakes generate information that allows corrective action later ... Actions taken and results observed may be only loosely coupled to one another. ...
5. Time stress: ... decisions are made under significant time pressure.
p.11 This volume is concerned with decisions made by individuals who know a lot about the problem domain.
p.11 when the task requires problem structuring, interpretation of ambiguous clues within the expert's domain, and reliance on underlying causal models, experts surpass novices who lack the knowledge base to guide their performances (Johnson, 1988).
p.14 The critical factor seems to be problem representation. As Hubner (1986) put it, "Decision behavior seems to depend on the decision maker's representation of the system, and the goal(s), plans, actions, etc. which are based upon the representation and goal(s)" (p. 121).
p.14-15 Anderson (1990) ... contends that the human cognitive apparatus, including perception, classification, memory, and problem solving, has evolved adaptively to cope with certain kinds of tasks found in everyday environments. Thus, to understand cognition we must understand the demands and critical features of the environment as they relate to cognitive processes.
.. There are the puzzles and games, such as chess, Tower of Hanoi, Rubik's cube and the eight puzzle ... Such problem solving has little adaptive value, and one can question whether our problem solving machinery has evolved to be adapted to such tasks. Indeed, one might argue, in the case of puzzles and games, that they are played because they are challenging, and they are challenging precisely because we are not adapted to succeed in such domains. (p. 192)
p.18 The major factor that distinguishes experienced from less experienced decision makers is their situation assessment ability, not their reasoning processes per se (Chi et al., 1988; Klein, 1989; Orasanu, 1990). Experts in a field can look at a situation and quickly interpret it using their highly organized base of relevant knowledge. The identification of situation type carries with it retrieval of one or more action alternatives that constitute appropriate responses.
p.18 A critical feature of the schema-driven approach is that people create causal models of the situation.
p.19 it appears that in complex realistic situations people think a little, act a little, and then evaluate the outcomes and think and act some more (cf. Connolly & Wagner, 1988). This decision cycle approach reflects incomplete knowledge, dynamically changing conditions, and competing goal structures that characterize NDM situations... The decision cycle approach treats the development of this knowledge as an integral part of decision making.

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