Systematic Political Science

The Social Simulation Strategy of Person(s):
Noncomputerized Validation and Verification of Analysis by Red Teaming Dominated and Undominated Options

by Dallas F. Bell, Jr. 

(Vide: Social Simulation Sequencing:  Constructing the Software Architecture for Systematic Political Science)

The Case for Red Teaming

In a contest a person is shown three shells lying on a table.  It is explained that only one shell has a pea hidden under it.  The contestant will win a prize if he or she correctly chooses the one shell that hides the pea.  After the contestant has chosen a shell, the host removes one of the remaining two shells to reveal that it did not have the pea.  The host then asks the contestant if he or she would like to switch their choice to the last shell.  The payoff is the same but switching has a win probability of two-thirds, the dominated option, and a one-third probability to win, undominated option, by not switching.  That reality is not always apparent to everyone until the contest is expanded to three hundred shells with a pea hidden under only one shell.  Your choice of shell A has a one in three-hundred probability of having the pea and winning the prize.  All the remaining shells, except shell B, are turned over to reveal that they did not contain the pea.  Aided by the tool of Bayes' theorem, it is virtually certain the pea is under shell B.  Granted that a one in three probability of winning is more favorable than a one in three-hundred probability of winning, the principle of switching to the dominated option has the consistently highest probability of a payoff.  Reinforcing the understanding of those odds may enable the intuition threshold, a person feels that choice A is better than choice B, to be surpassed.   

The risk threshold is defined as the point that the risk of choosing and loosing is perceived less than the risk of choosing and winning.  For example, a lottery sum is increased to a point that spending the money for a ticket to win is perceived to outweigh using the funds for something else.  Minus, of course, the presence of a primary disorder such as pathological gambling and any comorbity or simultaneous existing relevant medical condition. The risk threshold is lowest for those in the survival need level.  Even those in the next need level, economic security, may elect to play the lottery and forgo using the money to increase their interest bearing saving's account at the bank.  Lotteries are widely recognized as being funded largely by those least able to afford the probable loss.   

In California, it is estimated that there is a one in eighteen-million probability of winning the lottery.  Those odds are the same as finding one specific foot of ground between the over 3,400 miles between Portland, Oregon, and Acadia National Park, Maine.  Furthermore, to drive ten miles or more to buy the ticket for the California lottery results in a three times higher probability of the buyer being killed in an auto accident than the probability of winning.  Politicians, businesses, and other advertisers attempt to enhance a perception within their target audience toward their dominated option--propaganda.  Often conspiracy theories abound contrary to evidence for the probability of an undominated option occurring.  Because conspiracy theorists wrongly conclude that the odds for a reality x leading to a y outcome are perceived to be less than their alternative explanation.  

Some logical fallacies are categorized by the term of gambler's fallacy.  They include the misconception that a random event is more likely to occur because it hasn't happened for a period of time or a random event is less likely to occur because it hasn't happened for a period of time, and a random event is more likely to occur because it happened recently or a random event is less likely to occur because it happened recently.  Those false ideas are affected by illusion of control and math errors such as thinking a sequence of numbers is more or less likely to occur etc.  The T2 and T3 theological beliefs in astrology or a lucky/karma force or psychic abilities etc. are commonly observed.  There is ample empirical evidence that those deified concepts are untrue.  Thus, neither a T2 or T3 theology could be considered a viable dominated or undominated option, yet their believers refuse to switch to the dominated theological option of an infinite God, T1.  There are also conjunction errors explained as the thought that a case is more probable if another condition is added despite the reality that the extra aspect makes the case less probable.  This is seen in the experiment where 85 percent of the people chose the statement, Linda is a bank teller and active in the feminist movement, as being more likely than the statement that Linda is a bank teller.     

J. Edward Russo and Paul J. H. Schoemaker wrote that decision making should avoid traps of not generating or considering undominated alternatives to dominated options (Ralph Keeney and Howard Raiffa), inadequate information, using irrelevant information, and frame blindness which could include deciding to solve the wrong problem.  In 1981, Amos Tversky and Daniel Kahneman demonstrated that framing, or the manner that a problem is presented, can affect the outcome.  A group of participants were asked to choose between two United States programs to prepare for an Asian disease expected to kill 600 people.  Program A would save 200 affected people and program B had a one-third probability that 600 people would be saved and a two-thirds probability that no people would be saved.  A total of 72 percent of the participants were risk averse and preferred A while 28 percent preferred B.  A second choice was given between program C where 400 people may die or program D where there was a one-third probability that no one dies and a two-thirds probability that 600 people will die.  This time 78 percent preferred the risk-taking option of D and 22 percent chose C.  The programs were technically identical though a change in the decision frame between the two choices produced a preference reversal. 

Cognitive bias is considered to be prominent in the decision making process.  This involves undue optimism or pessimism, peer pressure and group think, experience limitations, and a premature ending of a search for evidence.  The anterior cingulate cortex and the orbitofrontal cortex are the brain regions considered to process decision making.  Distinct patterns of neural activation have been recorded in those locations to be dependent on whether decisions were made on the basis of personal volition or due to following orders from another entity. 

Tversky and Kahneman have identified four assumptions in decision theory regarding utility functions.  The first is cancellation, eliminating irrelevant alternatives.  For example, if A is preferred to B then A should be preferred if it rains next week to B if it rains next week, unless desiring to play golf makes them more weather dependent.  The second is transitivity.  If A is preferred over B and B is preferred over C, then A must be preferred over C.  Douglas J. Navarick and Edmund Fantino found that choice in concurrent-chain procedures can violate stochastic transitivity that is required to make unidimensional scale reinforcements possible.  The third assumption is dominance with the highest being stochastic dominance.  An option may be said to dominate another option if in every possible state it has an outcome perceived to be at least as good.  A choice is considered to be stochastically dominate over another choice if given all probability it has a perceived superior outcome.  The final assumption of expected utility theory is invariance. This means that different representations of the same problem should yield the same results.  The failure of that idea was demonstrated by the Asian disease problem presented earlier in this paper. 

The biblical Hebrew King Solomon stated that safety is found by having a multitude of counselors.  That was true thousands of years ago and is true today.  Using the template of systematic political science, people can explore all the known options and eventually make decisions with the most preferred outcomes.  This process should include robust give-and-take among all the players and is often called red teaming.  Therein rests the historically proven best hope of avoiding the analysis pitfalls inherent to finite decision makers. 

Red Teaming

Years ago military leaders practiced war maneuvers against a surrogate opposition force, OPFOR, composed of friendly soldiers.  Traditionally, the OPFOR was designated in simulation planning as the red team and the resident force was called the blue team.  Controllers established and monitored the parameters of the tasks and conditions, or cognitive model, for the exercise.  At the end of each event reports were written concerning the things that worked well and things that needed changed.   

Today red teaming is practiced by many organizations and has more than a surrogate mission.  Like the other two social simulation strategies of computers only and combinations of computers and persons, the social simulation strategy of persons comprise the simulation scope of microsimulation, simulation of an individual(s), intermediatesimulation, simulation of groups of individuals that make up an institution(s), or a macrosimulation, simulation of the institutions that make up a nation-state(s). The simulation scenarios are either specific where the number and types of agents are known by time and environment of action, or they are nonspecific where the number and/or type of agents nor the time or environment of action is specifically known. 

J. Edward Russo and Kurt A. Carlson have identified five phases of the decision process.  The first is the representation of the decision task.  The second phase is the generation of alternatives.  The third is the acquisition and evaluation of information. Phase four involves the resolution of multiple units of information to distinguish one alternative as superior.  Lastly, five is post-commitment distinction, implementation, and learning. 

Russo and Carlson's systematic process recognizes the necessity of first finding and categorizing dominated and undominated options.  This is the beginning goal of the red teaming mission of validation.  Independent of the blue team the red team should create and collate options.  When that phase is determined to have been accomplished the red team can generate more alternatives using the blue team's conclusions and vice versa.  Acquiring and evaluating information is the red team's operation for the third phase. 

Another red teaming mission is one of verification.  It serves to transition phase three into phase four which is the resolution of units of information to distinguish one alternative as superior.  In the last phase the red team challenges the implementation, learning, and training of the results. 

For surrogate missions, a red team should be composed of those persons operating with the same need levels and problem solving skills, META formulae, as the OPFOR they are simulating.  Whatever the red team operation may entail, its makeup and equipment should be adjusted accordingly.  The art is generally to mix those with different skills to maintain the forensic aspect within the red team itself.  

Diane Vaughan and others have argued that management decisions tend to become routine and evolve over time.  An active red team can help to insure that the erosion of standards by decision makers is minimized.  Red teaming is not a cure-all.  The better the red team the better the input will be for the blue team's action and reaction.   

Another red team concern is known as the observer effect.  Merely by observing causes behavior to change.  Therefore, behaviors of both red and blue teams can be expected to be modified along with the true oppositional force(s).  Also, the (Heisenberg) uncertainty principle implies that it is impossible to assert by position and motion that a particle is at the same time at a point and moving with a specific velocity.  This quantum observation can be applied to a red teaming surrogate operation.  The enemy's geographic positions and decision-tree will always be considered difficult to simultaneously determine.  

The challenges for implementing and maintaining red teaming operations are admittedly great.  However, the investment offers low risk and high reward for those that want the best analysis to consistently make superior decisions in a highly competitive world.  Chapter four and verse six of the Old Testament book of Hosea addresses the alternative by declaring, "My people are destroyed for lack of knowledge..."