Predictive Cueing for Crisis Decision Support
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Solution Overview
Problem
In crisis decision-making situations, traditional rational choice processes are ineffective due to time constraints, insufficient information, and rapidly changing circumstances, making it difficult for decision-makers to evaluate options and adapt to changing conditions.
Innovation Solution
A decision support system that uses a recognition-primed decision process, employing a pattern recognition engine to identify patterns from known cases and provide predictive prompts, allowing decision-makers to quickly select 'good enough' options based on initial size-up information and iteratively refine decisions through cue analysis, using an artificial intelligence engine like a neural network to narrow down decision outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a rational choice decision process is used to evaluate all options and information, then decision quality may be improved, but time consumption increases significantly making it ineffective in crisis situations
Solution Approach 1:
The system pre-processes and stores decision cases and their attributes in a knowledge base before crisis situations occur. During crises, the system quickly retrieves and matches relevant pre-analyzed cases based on initial situation characteristics, eliminating the need to perform complete rational analysis from scratch under time pressure.
Solution Approach 2:
The decision process is segmented into distinct phases: initial rapid pattern matching to identify relevant cases, followed by iterative refinement through predictive prompting. This breaks down the overwhelming rational choice process into manageable steps that can be executed sequentially under time constraints.
2Measurement precision
If complete information gathering is performed to characterize each option, then decision accuracy improves, but the ability to keep up with changing circumstances deteriorates
Solution Approach 1:
The system employs iterative predictive prompting that dynamically adapts to changing circumstances. After each user response to a predictive prompt, the system re-evaluates and generates new predictions, allowing the decision process to evolve with changing information and conditions rather than requiring complete static analysis upfront.
Solution Approach 2:
The system gathers information partially and iteratively rather than attempting complete information gathering initially. It focuses on obtaining just enough information to identify relevant cases and make progress, then continues gathering information as needed, rather than requiring all information before acting.
3Device complexity
If traditional analytical approaches are used to evaluate all options, then comprehensive decision analysis is achieved, but speed of decision-making decreases
Solution Approach 1:
The system introduces an artificial intelligence intermediary that performs preliminary pattern matching and case retrieval between the user and the full decision analysis process. This AI intermediary filters and pre-processes information, presenting only the most relevant cases and predictive prompts to the user, thereby speeding up the overall process while maintaining analytical depth.
4Reliability
If detailed evaluation of each option is performed, then decision reliability improves, but the time frame allotted by crisis circumstances is exceeded
Solution Approach 1:
The system performs preliminary pattern matching and case retrieval based on initial situation characteristics before detailed evaluation is needed. This pre-processing identifies the most relevant cases and potential decision options, so that when detailed evaluation is performed, it focuses only on a narrowed set of promising options rather than all possible options.
Solution Approach 2:
The system performs partial evaluation iteratively, focusing detailed analysis only on the most promising options identified through initial pattern matching and predictive prompting, rather than evaluating all options in detail. This allows reliable decision-making to proceed within the constrained time frame by concentrating resources on the most critical evaluations.
Data Source
AI summary
A method and system that provide for decision support and/or training support in crisis decision-making situations are provided. In one implementation, a method identifies patterns from known cases based on information from a crisis event. Each of the known cases includes attributes and at least one outcome. The method also identifies a first subset of the known cases that relate to the identified patterns from the known cases. The method also analyzes the identified patterns to determine a cue that, if answered, will provide a second subset of the known cases including a more converged range of decision outcomes than the first subset.