Page 147 - DCAP310_INTRODUCTION_TO_ARTIFICIAL_INTELLIGENCE_AND_EXPERT_SYSTEMS

Unit 7: Probabilistic Reasoning

arrive at their opinions rather than specific knowledge of biotechnology. Specifically, the Notes
heuristics they used were deference to scientific authority, trust in scientific institutions, and
whether they had seen media coverage of biotechnology. Different heuristics were used for
different demographic groups, and actual knowledge of biotechnology played a small role in
opinion formation.
This also brings up the idea of using current media news as a heuristic. Whatever information
has been most recently presented by the media is likely to be more accessible in an individual’s
mind. This information can then be used as a shortcut in evaluating an issue, and is used
heuristically in place of lengthier cognitive processing using past information.

Our use of heuristics may come directly from individuals. Opinions of trusted or elite individuals
may themselves become a heuristic. When evaluating a decision or problem, individuals can
turn to these trusted or elite individuals for their opinions. Rather than evaluating the information
surrounding the decision, the individual uses these trusted opinions as informational shortcuts
to make their decisions.

7.3.2 Example of D – S Application

Heuristics used when forming opinions can also be ideologically based. A 2008 study looked at
the relationship between religion and opinions about nanotechnology. This research found that
the more religious the citizens of a country, the less likely they were to support nanotechnology.
This suggests that people used religion as a shortcut or heuristic; they were not informed about
nanotechnology, but because their religious beliefs cautioned them against some forms of
technology, they used an ideological heuristic to form their opinions about an unknown
technology.
Different individuals use different heuristics to process the information before them based on
their available schema and the framing of the information. Issues may resonate with different
schemata depending on the individual and the way the issue is framed.

Example: “Drilling for oil” may activate schemata relating to corporate profits,
environmental disasters, and exploitation of workers, while “exploring for energy” may activate
schemata related to protecting the environment, American pride, and American innovation.
These two terms refer to the same activity, but when they are framed differently, different
schemata are activated, which results in the use of different heuristics.

7.3.3 Combining Evidences

In computer science, a heuristic is a technique designed for solving a problem more quickly
when classic methods are too slow, or for finding an approximate solution when classic methods
fail to find any exact solution. By trading optimality, completeness, accuracy, and/or precision
for speed, a heuristic can quickly produce a solution that is good enough for solving the problem
at hand, as opposed to finding all exact solutions in a prohibitively long time.

Example: Many real-time anti-virus scanners use heuristic signatures for detecting viruses
and other forms of malware. One way of achieving this computational performance gain consists
in solving a simpler problem whose solution is also a solution to the more complex problem.
Heuristics is used in the A* algorithm whose intent is to find a short path from one node to
another. A high-value heuristic computes a path quickly, but the path might not be the shortest.
A low-value heuristic computes a path more slowly, but the path becomes shorter.

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