Intuition, Insight, and Degrees of Solution

Intuition, Insight, and Degrees of Solution

Our problem solving process is a semi-circular process embedded within a chronological scale. This includes a repetitive similarity of process yet not of data analyzed. Each cognitive analytical event evaluates, compares, reasons, then moves to the next cycle quickly running through large and small data sets within a much larger set, and with all hopefully relevant to the problem set to be solved. A recursive process iterates along with a goal of solution, but of course, not to be confused with circular reasoning. But what is really happening?

Recursive inventory analysis may at some point tip towards a tentative hypothesis of uniqueness and by insidious criminal association… individualization. You just can’t separate the uniqueness from the individualization, nor the crime from the criminal. But how do we measure the information needed for such a hypothesis? How do we know that we have this tipping point, this minimally sufficient information that allows us to understand individualization, a moment of positive recognition in context, when “No predetermined number of friction ridge features is required to establish an individualization”? [1] We usually try to articulate some sort of ratio between quantity/quality of information as compared to the lack of it. An intuitive ratio? When we reach that tipping point we can typically illustrate such a feat with a simplified point chart. In most cases it is a level 2 chart that marks points of interest with red dots. These are points that we, in part, analyzed. Can it be said that we intuitively know we have reached the minimally sufficient information that supports a hypothesis of individualization? If the tipping point is relative to the examiner’s ability to evaluate the problem set, and the analysis is considered a unique development (non-specificity), it can’t be otherwise can it? This is fundamentally, degrees of solution. One examiner will require more or less information to solve the problem. The important point is that they each successfully surpass the minimally sufficient relative threshold. Of course, any additional analysis beyond this threshold would add hypothesis support and lower uncertainty. An analogy is that we don’t need to compare each and every minutia of a palm print to form a reliable hypothesis.

We must admit that to understand what entails a “minimally sufficient” information set, which allows us to recognize individualization when it exists, is a cumulative compilation of our experience and our capability applied to that study of that problem. Therefore, it must also be based, to a significant degree, on intuitive probability. As there is no sharp well defined threshold, as each examiner will have the potential to reach a “minimally sufficient” tipping point from their nonlinear analysis according to their available information and relative ability to correlate and comprehend that information at that particular point in time. Of course, as the analytical process progresses one’s knowledge also changes. The old saying of “you can never step in the same river twice” also applies to our cognitive process. Our mental database is always changing from moment to moment, and year to year. We gain new knowledge and forget some as well. What is the probability of my hypothesis being correct? As this is not computable to any sufficient degree of accuracy we are leveraged (forced) to rely on our ability to calculate probability on an intuitive level while we study our rough probability models in hope that they are at least reasonably accurate.

In a nutshell, the solution, at a minimally sufficient threshold, has the potential to be discovered holistically and recursively from the inter-related informational problem set while drawing from related experience such as training and practice. In the end, a hypothesis is made of this newly discovered solution that we can illustrate is correct based on past experience and the concept of uniqueness. How we adjust our applied knowledge at specific points within the problem solving process is also critical in our ability to efficiently solve problems. If one particular approach does not work, then we need to recognize that it may be due to our process not the lack of information present. An examiner who keeps using a certain incorrectly acquired target group, may reach a conclusion of No Match. In fact several examiners may use this same false key, whereas the solution was to use a different starting point as subtle distortion has had is effects. With such a scenario it could be said that both the comparison process was incorrectly applied, and the intuitive probability assessment was based on false information. It illustrates the potential limits of the human element in our process. This is an error to be sure, yet is not evidence proving the concept of individualization is inconsistent. Had the examiner(s) had the insight to try a new target group, they most likely would have found the solution.

“Modern research on problem solving has continued to wrestle with emergent phenomena, such as insight. In the dominant modern formulation, discontinuous change in problem-solving behavior is assumed to involve a restructuring of an internal representation of the problem.” [2] How do we categorize insight, emergence, and intuition into our problem solving process? Insight can be largely attributed to efficient utilization of our base knowledge relevant to specific relationships. In effect, a pool of information and experience we can successfully access and understand in context as needed. Thus, insight can be the ability to see options. With this outline it is easily to see how insights can be limited and unrealized.

Emergence can also be described as; “The rapid appearance of novel structure” within the recursive process. “Examples of emergent cognitive structure can be trace back to early Gestalt research in problem solving.” [3] In this light emergence can be thought of as strategic application and is directly related to insight. While forensic comparison may not seem to have the need for significant degrees of strategy, the base comparison search would. That false negative conclusion may have been the result of poor strategy that failed to produce the needed insight. Or perhaps, our knowledge base and experience did not contain sufficient information for us to understand the relative connections, thus solve the problem.

I think it is safe to say that we do indeed use intuition to understand degrees of uncertainty in our evaluations. We must, because we can’t run the hard numbers. We are not allowed to run the numbers. We simply do not have all aspects and variables quantified however, we may indeed have the information we need to solve the problem itself even if we don’t realize it.

I see no way to shake the “intuitive probability” aspect, since we cannot have all the variables inventoried and well understood. We cannot even make a precise prediction regarding the degree of uncertainty within any of the relevant variables. Sounds catastrophic however this is not a bad thing and could even be considered perfectly normal with this type of problem solving. It is just that most critics want, and in some cases, demand solid numbers for their comprehension of proof. I consider this ignorance on their part rather than a weakness on the part of the examiners. While we will never be able to identify and calculate all the aspects of uncertainty, we can with proper training and experience, minimize their negative impacts on our accuracy and get the job done. Uncertainty, including the application of intuition, does not mean a hypothesis is not accurate; it simply means we can’t understand and utilize all the information within a real world problem set. A hypothesis of individualization is based on a non-algorithmic nonlinear assessment and is made in the face of uncertainty, albeit with the odds generally on our side. Absolute proof is an eccentric mathematical concept that seems to exist simply to cause us mere mortals mental grief. Luckily we don’t have much use for absolute proof. Intuitively, less than absolute seems to work just fine in the real world.

I like the concept of breaking things down to better understand their sub-systems and components, yet the best approach may be a recursive one that studies relationships and insight within a framework of education and experience coupled with intuitive probability. Informational sets and processes are revisited and insights applied as new relationships are better understood in context during our investigation of the problem. Many processes seem to only work as interrelated groups and by themselves, may simply become novelties. In our motorsport analogy of a few posts back, the problem set was to navigate a lap of a race track. This problem set, not to unfamiliar to any other problem set, is full of relationships and groups of interrelated actions that surge within a chronological order that subsequently must work in concert to solve the problem. I would think this is scalable to the simple problem of comparing two prints. There are many relationships and groups of inter-related data that when separated out has little value, but in particular relationships, means everything. A key is to comprehend the value of the relationships and interrelationships as one works through the problem. Again, I think this value of these relationships must be assigned intuitively.

Perhaps specific training in probability will help. I understand general research has shown that our intuitive probability skills are underdeveloped.

Craig A. Coppock

Reference:

[1] SWGFAST: STANDARDS FOR EXAMINING FRICTION RIDGE

IMPRESSIONS AND RESULTING CONCLUSIONS. 100910 draft

[2] The Self-Organization of Insight: Entropy and Power Laws

in Problem Solving; Damian G. Stephena,b; James A. Dixona,b,c

From: (Bowden, Jung-Beeman, Fleck, & Kounios, 2005; Chronicle,

MacGregor, & Ormerod, 2004; Fleck & Weisberg, 2004; Gilhooly & Murphy, 2005; Knoblich,

Ohlsson,& Raney,2001). Insight entails an observable discontinuity in a solver’s approach

to a problem indicating a restructuring of the solver’s representation of the problem;

(Chronicle et al., 2004; Weisberg, 1996)

[3] The Self-Organization of Insight: Entropy and Power Laws in Problem Solving. Damian G. Stephen/ James A. Dixon