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The fiction of optimization and deliberate practice, A Decision/Action Model for Soccer – Pt 11
 

The fiction of optimization and deliberate practice, A Decision/Action Model for Soccer – Pt 11

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A Decision/Action Model for Soccer – Pt 11, The Fiction of Optimization and Deliberate Practice, Removing Barriers to Expertise. ...

A Decision/Action Model for Soccer – Pt 11, The Fiction of Optimization and Deliberate Practice, Removing Barriers to Expertise.

“The perfect is the enemy of the good” – Voltaire
“A good plan violently executed now is better than a perfect plan executed next week.” - George Patton

“The concept of optimization relies on a number of assumptions. These assumptions are very restrictive. I have not met any decision researcher or analyst who believes that these assumptions will be met in any setting, with the possible exception of the laboratory or the casino… In the majority of field settings, there is no way to determine if a decision choice is optimal owing to time pressure, uncertainty, illdefined goals, and so forth.” Gary Klein

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    The fiction of optimization and deliberate practice, A Decision/Action Model for Soccer – Pt 11 The fiction of optimization and deliberate practice, A Decision/Action Model for Soccer – Pt 11 Presentation Transcript

    • A Decision/Action Model for Soccer – Pt 11 The Fiction of Optimization and Deliberate Practice Removing Barriers to Expertise “The perfect is the enemy of the good” - Voltaire “A good plan violently executed now is better than a perfect plan executed next week.” - George Patton “Optimization refers to the attempt to find the best option out of potential courses of action… (Simon) defined optimization as the selection of the best choice, the one with the highest expected utility.” Gary Klein [14] “The concept of optimization relies on a number of assumptions. These assumptions are very restrictive. I have not met any decision researcher or analyst who believes that these assumptions will be met in any setting, with the possible exception of the laboratory or the casino… In the majority of field settings, there is no way to determine if a decision choice is optimal owing to time pressure, uncertainty, illdefined goals, and so forth.” Gary Klein [14] 1
    • The boundaries of an optimization process “This chapter lists a number of barriers to selecting an optimal course of action and further asserts that optimization should not be used as a gold standard for decision making.” [14] 1. “The goals must be well defined in quantitative terms. If the goals cannot be measured, then there are no metrics for use in comparing courses of action.” [14] 1. • 2. This barrier eliminates qualitative and emotional assessments. No gut feelings. “Best” has to have measurable qualities i.e. touches, score, tackles, turns, processes, records and times. Youth tryouts are famous for having mystery numbers. “The decision makers values must be stable. Optimization assumes that tastes and values are absolute, relevant, stable, consistent and precise.” [14] 1. • 3. This barrier assumes that expectations and standards will not change. What held yesterday will hold today, tomorrow, forever. Furthermore these values are shared by all of the stakeholders involved in the action. “The situation must be stable. In practice, if feedback from the outcome of the decision sequentially affects the implementation, this moves the task outside the boundary conditions for decision analysis.” [14] 1. • Open systems, those in ‘the field’ are naturally unstable due to uncertainty, friction, chance, time constraints and unfolding events. 2
    • The boundaries of an optimization process “This chapter lists a number of barriers to selecting an optimal course of action and further asserts that optimization should not be used as a gold standard for decision making.” [14] 4. “The task is restricted to selection between options. Optimization requires that we restrict the task to… ‘the moment of choice’.” 1. • 5. [14] Choosing is a binary action, yes to one, no to all others. An either/or moment made in an unstable environment with shifting values, standards and expectations. “The number of alternatives generated must be exhaustive. Unless we have considered all reasonable options we cannot be sure that we have selected the best.” [14] 1. • 6. This assumes that the person making the decision is omniscient, a standard that cannot be attained outside of small-world, closed system descriptive models. “The optimal choice can be selected without disproportional time and effort. 1. If the optimal choice is barely superior to the closest alternatives… The costs of obtaining it offset the advantages it provides.” [14] • Fredkin’s paradox “concerns the inverse correlation of the difference between two options and the difficulty of deciding between them. ‘The more equally attractive • two alternatives seem, the harder it can be to choose between them -- no matter that, to the same degree, the choice can only matter less’.” Wikipedia Furthermore, opponents will violently oppose your efforts which decreases cognitive resources and increases the time needed for deliberation. 3
    • The boundaries of an optimization process “This chapter lists a number of barriers to selecting an optimal course of action and further asserts that optimization should not be used as a gold standard for decision making.” [14] 7. “The options must be thoroughly compared to each other. This entails comparing each option to every other option across a full set of dimensions.” [14] 1. • 8. Each option generated by the omniscient process in 5 must be compared against every other one. While these comparisons are being made situations and values change to accommodate new information, feedback from previous actions and unfolding interactions. [4, 11, 19] “The decision maker must use a compensatory strategy. The comparison needs to take into account the degree of difference between each option, not simply that a difference exists.” [14] 1. • “All you see is the turn, you don’t see the road ahead” from Stand and Deliver, http://www.youtube.com/watch?v=CtYiJX_rxEY. 9. “The probability estimates must be coherent and accurate. Probability estimates for different outcomes must be reasonably accurate.” 1. • [14] Optimization models can’t allow for a close enough for government work answer or an emotional response even when it’s called for i.e. “creative desperation.” [14] This boundary reinforces the need for rational, analytical methodology. 4
    • The boundaries of an optimization process “This chapter lists a number of barriers to selecting an optimal course of action and further asserts that optimization should not be used as a gold standard for decision making.” [14] 10. “The scenarios used to predict failure must be exhaustive and realistic. If the sequels [of scenarios] are shallow, the analysis cannot be very good.” 1. • 11. [14] Is it soccer, soccer like or soccer strange? Is friction included? The farther the scenario is from the game, the less friction is employed, the weaker it will be i.e. running laps or juggling a ball are weak scenarios, basic 4v4 is strong. [18, 30] “The evaluation scenario must be exhaustive. Once the scenario has been generated, the examination must be rigorous… all possible outcomes must be considered. In field settings, this would be unrealistic.” [14] 1. • This requires time and consideration of every possible variation. “While there is some benefit in having a gold standard as a yardstick for assessing adequacy of our deliberations, there are drawbacks as well. The review of assumptions listed [above] makes it clear that very few decision problems will permit optimization, outside of the limited context problems presented in laboratory studies.” [14] 5
    • Looking between the bookends Best answer, worst answer or something in between “Why do researchers and practitioners cling so tenaciously to the gold standard of optimization? One reason is that the concept of maximization rests on a faith in mathematics. Once the mathematical formulation for expected utility theory was obtained the agenda for researchers and practitioners seemed clear: to find ways to translate decisions into the appropriate formalism… Because maximization is based on mathematical proofs, these theorems act as a bedrock. Few areas in behavioral science can boast such a solid basis for investigation.” [14] “Once we abandon optimization as a goal, the issue of training takes on new possibilities. Previously, training was seen as instruction in the process of optimization. Instead of teaching [or teaching with] analytical strategies, we may be more effective in building up the expertise needed to handle [a] resource-intensive process.” [14] This implies a move away from preparation, the effort of ‘getting ready’ to readiness itself. Preparation never ends, readiness is what is brought onto the field at the moment of need. 6
    • “What makes for better decision making?” [24] “Is the ‘best’ option the ‘right’ one?” [24] “We can begin then by identifying the two basic ways of solving a problem: intuitive and effortful.” [24] Intuitive (or naturalistic)         Naïve intuition: the traditional street education, school of hard knocks. Characteristics; Informed by ‘common sense’ and experience in other contexts. Possible shortcomings; Especially sensitive to psycho-social factors like group think. Good for; Expert decision makers where time is short. Educated intuition: Klein’s recognition-primed decision making. Learning expertise. Characteristics; Informed by specific expert strategies – mental simulation, prototypical models; expectancies; cues; singular evaluation approach. Possible shortcomings; ‘Satisficing’ e.g. settling for good enough. Missing the bigger picture. Good for: Situations that don’t merit a time consuming effort but must be reflected on. Effortful (or rational): Optimization.    Characteristics; Informed by rational, logical thinking making use of formal problem solving tools. Possible shortcomings; Unnecessary effort; slow; resource intensive; open to overload. Good for; Novice decision makers; all decision makers where they have time and the decision is both simple and important enough to use this approach. [24] 7
    • “Problem types” [24] Matching the strategy to the situation “Effective problem solving often hinges on recognizing the type of problem that is being faced. In general terms, there are four main problem types: simplistic, deterministic, random and indeterminate (or ‘Wicked’):” [24]     “Simplistic problems: where there is one and only one answer. For example, who is the goalkeeper? Deterministic problems: where the only answer is arrived at by the application of a formula, algorithm or protocol. For example, the circumference of a circle is found by applying a certain formula. In soccer, offside. Random problems: where there is only one answer, but there are a number of possible correct answers. For example, who will win the EPL this year? Indeterminate problems: where the answer itself is complex, hard to identify or changes in time. For example, what is ‘success’ in a particular season? Answering such a question means taking into account a huge range of factors including how others see the issue, how the issue has changed and how your earlier decisions and actions have themselves affected the issue. To use Rittel and Webber’s terminology, these are ‘wicked problems’.” Selecting players at a tryout, especially when there’s very little difference between them, again Fredkin’s Paradox. (In the real world a numbering system, (1 to 5) is used over different categories (passing, dribbling) to justify a subjective evaluation (the evaluators gut feeling).) “Numbers don’t lie Mrs. Jones. Your sons a 3, average.” Modified from the original text [24] to soccer. 8
    • Types of problems and the role of facts and judgments [24] Matching the strategy to the situation “When faced with a problem it is useful to categorise and ask: ‘what kind of problem is this?’ Once identified, we can apply the most appropriate problem solving process. As the diagram shows, in both simplistic and deterministic problems, facts and algorithms are applied; judgment is not… you simply use the appropriate SOP. However, random problems entail both facts and judgment. To return to the question of the EPL winner, although it is possible that any of the teams will win, pertinent evidence from performance, and reports etc., enables us to narrow the field down considerably. In dealing with indeterminate problems, ‘facts’ are likely to be rejected or contested by the various stakeholders making their use of little value or, worse, counter-productive, particularly when those ‘facts’ are culturally derived.” [24] Standardized curriculums based on optimizing models teach facts and algorithms towards a predetermined point. Learning curriculums build expertise by allowing individual judgments to be tested against reality; trial, error, reflection and repeat. Reading the game requires judgment. [24] 9
    • Learning expertise – using judgment “Very narrow areas of expertise can be very productive. Develop your own profile. Develop your own niche. “ – Leigh Steinberg “After reviewing the the literature, I identified a number of ways that experts in different fields learn.” Gary Klein [15]  They engage in deliberate practice,* so that each opportunity for practice has a goal and evaluation criteria.   They compile an extensive experience bank.   Feedback includes friction, competition, cooperation, the self, emotional, rational, physical, moral elements that are frequently exposed to uncertain changes. This creates events which carry “emotional ballast” and set up a clear demarcation between process and product. [18] They enrich their experiences by reviewing prior experiences to derive new insights and lessons from mistakes.   Experts have width and depth in the memories available for recall and recognition. These reified ‘deposits’ should be easy to combine quickly into different configurations. Adaptability involves the process of rapidly destroying old memories and creating new ideas out of the parts. [2, 22] They obtain feedback that is accurate, diagnostic, and reasonably timely.   Deliberate practice in soccer centers on scoring and preventing goals, not completing a decontextualized teaching curriculum. Scoring or denying a goal, winning, is the desired end state of the process. Without that, the feedback that’s filling the experience bank can’t be trusted. After action reports and spontaneous intra-event communication. Players must be questioned, and honestly self-question about the events that just took place. Mental models are shared and debated which enriches individual points of view against the “wire brush” of other opinions. Moral courage is also developed in this process. [27, 28] * Slides 13, & 14 deal with deliberate practice. 10
    • Heuristics, building blocks for expertise Working rapidly in the space between best and worst “My own research on recognitional decision making has resulted in a model that essentially blends three of the heuristics described by Kahneman and Tversky… availability, representativeness, and the simulation heuristic… Experienced decision makers are able to categorize situations rapidly as typical of various prototypes, using representativeness [familiarity] and availability heuristics [importance], and are able to evaluate courses of action suggested by these prototypes by conducting mental simulations, using simulation heuristic [imagination, progressive deepening], without having to compare options.” [14] “Because the key to effective decision making is to build up expertise, one temptation is to develop training to teach people to think like experts. But in most settings, this is time consuming and expensive. However, if we cannot teach people to think like experts, perhaps we can teach them to learn like experts.” [15] Use learning curriculums as opposed to teaching curriculums. 11
    • “Notes on the Psychology of Expertise” “You must continue to gain expertise, but avoid thinking like an expert.” - Denis Waitley “An expert is not necessarily more gifted than other individuals. Psychologists use the term expert to refer to an individual who is significantly more experienced than others in performing a particular task. However, the difference between experts and novices cannot be reduced solely to experience (time invested in learning how to perform tasks). There are at least five qualitative differences between experts and novice.” 1. Novices rely on formal rules and procedures to guide them. Experts rely to a greater degree on their accumulated experience. 2. Novices are highly conscious of the task performance process. This is a distraction and creates additional "load" on cognitive processing. As expertise grows, performance of the task becomes automatic. This cognitive phenomenon is called "automaticity." 3. As expertise is acquired, the learner's cognitive processing system becomes more efficient at processing new information. As a result, experts can see the whole picture. They are also more aware of the specific circumstances in which they are working. They have good self-monitoring skills. Experts can make even very complex, difficult tasks look easy. 4. The expert has a larger number of strategies, and more effective strategies, for performing the task. This may be the most critical difference between the expert and the novice. Experts know how to get out of trouble because they have multiple strategies for dealing with the unexpected. 5. Experts are more flexible than novices. They rely on intuition in ways that novices find difficult to comprehend.” [1] 12
    • *Deliberate practice models are built on optimization “10,000 Hours Plus or Minus 10,000 Hours.” [7] “Scientists who study skill performance attempt to account for “variance” between people [elite vs. novice]. Variance is a statistical measure of how much individuals deviate from the average. In a sample of two runners, if one athlete completes the mile in four minutes and the other runs it in five minutes, then the average is four and a half minutes and the variance is half a minute [These players are +/- 30 seconds from the average]. The question for scientists is: What accounts for that variance, practice, genes, or something else?” [7] “It is not enough to say that practice matters… “There isn’t a single geneticist or physiologist who says hard work isn’t important. Nobody thinks Olympians are jumping off the couch.” [7] “Scientists must go beyond saying that practice matters and attempt the difficult task of determining how much practice matters. By the strictest 10,000-hours thinking, accumulated practice should explain most or all the variance in skill. But that never, ever happens. From swimmers and triathletes to piano players, studies report that the amount of variance accounted for by practice is generally between low and moderate.” [7] 13
    • *Deliberate practice models are built on optimization “10,000 Hours Plus or Minus 10,000 Hours.” [7] “Scientists have increasingly realized that the inherited components of complex traits, the athleticism, is most often the result of dozens or even hundreds or thousands of interacting genes, not to mention environmental factors.” [7] Deliberate practice has become a gold standard for developing talent. But when something as complex as talent is reduced to a simplistic formula, like the one found in popular culture, the answer always falls short. The original, difficult question has been replaced unconsciously by a lesser, easier one. The idea behind deliberate practice; 10,000 hours, hard work, expert coaching and myelin production is an example of this. It’s a matter of attribute substitution, [12] where the indeterminate reality has been replaced by the theoretically simplistic. What had required judgment has been reduced to a system of algorithms, facts and rules.  “Attribute Substitution is a psychological process thought to underlie a number of cognitive biases and perceptual illusions. It occurs when an individual has to make a judgment (of a target attribute) that is computationally complex, and instead substitutes a more easily calculated heuristic attribute. This substitution is thought of as taking place in the automatic intuitive judgment system, rather than the more self-aware reflective system. This explains why biases are unconscious and persist even when the subject is made aware of them. It also explains why human judgments often fail to show regression toward the mean. Hence, when someone answers a difficult question, they may be answering a related but different question, without realizing that a substitution has taken place.” Dr. Martin Poulter http://biasandbelief.wordpress.com/2009/06/01/attribute-substitution/ 14
    • Summary Dropping optimization as the “gold standard” poses both a problem and opportunity for influencing decision making in soccer. The problem is that optimization is a deeply entrenched cultural belief. It is difficult to acknowledge, let alone change, largely unconscious thinking and results in social double binds. We’ll look at this in detail in Part 12, Culture in decision making. When optimization is replaced by something less, some of the opportunities are;           People have to rely more on their own judgments, people face moral and mental tests. Encourages trial-and-error; improvisation. Demands negotiation for collaboration which; Creates opportunities for learning adaptive leadership and expertise. Allows people to tackle random and indeterminate problems. Make it better, not perfect. Work at a faster tempo. Both teams can use the ‘first one to act wins’ rule. Avoids non-human technologies i.e. hard line SOP’s like zero tolerance policies and coaches mandates. Encourages fast and frugal heuristics as primary decision strategies. Allows ‘gut feelings’ to become the stopping point of decision making. Emotion has a say. System 1 learns from and updates System 2 through the dialog between participants i.e. intra-activity communication and After Action Reflection and debate. The value and experience gained from participation isn’t lost because a single correct process has to be followed. 15
    • References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. ARMSTRONG, J. 2003, Notes on the Psychology of Expertise (University of New Mexico: http://www.unm.edu/~jka/courses/archive/expertise.html). BOYD, J. 2005, Organic Design for Command and Control (http://www.dnipogo.org/boyd/organic_design.pdf). COLVIN, G. 2008, Talent is Overrated (London: Penguin Books). CORAM, R. 2002, Boyd, The Fighter Pilot Who Changed the Art of War (New York: Back Bay Books). COTE, J. BAKER, J. ABERNETHY, B. (2007) Practice and Play in the Development of Sport Expertise, Handbook of Sports Psychology 184-202; 3rd edition ( Hoboken NJ: Wiley). COYLE, D. 2009 The Talent Code, Greatness Isn’t Born, It’s Grown (New York: Bantam). EPSTEIN, D. 2013, The Sports Gene, The Inside Story of Extraordinary Athletic Performance (New York, NY: Penguin Books) FRANKLIN, S. 1997, Artificial Minds (London, England: Bradford Book). GLADWELL, M. 2008, Outliers, The Story of Success (New York: Back Bay Books). GROSSMAN, D. 2004, On Combat, The Psychology and Physiology of Deadly Conflict in War and in Peace (Warrior Science Publications). HAMMOND, G. 2001, The Mind of War, John Boyd and American Security (Washington D.C: Smithsonian Instition Press). KAHNEMAN, D. 2011, Thinking Fast and Slow (New York: Farrar, Straus and Giroux). KEAN, S. 2012, The Violinist’s Thumb, And Other Lost Tales Of Love, War, And Genius, As Written By Our Genetic Code (New York: Little Brown & Co.) KLEIN, G. 1998, Sources of Power, How People Make Decisions (Cambridge, Ma: MIT Press). KLEIN, G. 2001, The Fiction of Optimization – Bounded Rationality, The Adaptive Toolbox – Gigernezer & Selten Editors (Cambridge Ma: MIT Press) 16
    • References 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. KLEIN, G. 1998, Sources of Power, How People Make Decisions (Cambridge, Ma: MIT Press). LAVE, J. & WENGER, E. 1991, Situated Learning, Legitimate Peripheral Practice (New York: Cambridge University Press). MEMMERT, D. BAKER, J. BERTSCH, C. June 2010, Play and practice in the development of sport-specific creativity in team ball sports (High Ability Studies, Vol. 21, No. 1, 3-18). MICHELS, R. 2001, Teambuilding, The Road to Success (Spring City, Pa: Reedswain). OSINGA, F. 2007, Science, Strategy and War, The Strategic Theory of John Boyd (New York: Routledge). PIRSIG, R. 1974, Zen And The Art Of Motorcycle Maintenance, An Inquiry in Values (New York, NY: Harper Collins). RAO, V. 2011, Tempo, Timing, Tactics and Strategy in Narrative-Driven Decision-Making (Ribbonfarm Inc). RICHARDS, C. 2004, Certain to Win, The Strategy of John Boyd, Applied to Business (Xlibris Corporation). RITZER, G. 2008, The McDonaldization of Society 5 (Thousand Oaks Ca: Pine Oaks Press). ROYAL MILITARY ACADEMY SANDHURST 2011, An Officer and a Problem Solver: Developing Problem Solving and Thinking Skills in Officer Cadets at Sandhurst (http://www.army.mod.uk/documents/general/RMAS_An_Officer_and_a_Problem_Solver.pdf). STANTON, N. et al. 2006, Distributed situation awareness in dynamic systems: theoretical development and application of an ergonomics methodology (Ergonomics, Vol.49, Nos. 12-13, 1288-1311). TVERSKY, A. KAHNEMAN, D. 1974, Judgment Under Uncertainty: Heuristics and Biases (Science, New Series, Vol. 185, No. 4157, 1124-1131). VANDERGRIFF, D. 2006, Raising the Bar: Creating and Nurturing Adaptability to Deal with the Changing Face of War (Washington, D.C., Center for Defense Information Press). 17
    • References 28. 29. 30. 31. 32. 33. VANDERGRIFF, D. 2008, Building Adaptive Leaders: The Army can Adapt its Institution (pt.1) (www.smallwarsjournal.com). VAN LINGEN, B. 1997, Coaching Soccer, The Official Coaching Book of the Dutch Soccer Association (Spring City, Pa: Reedswain). WALKER, G. STANTON, N. JENKINS, D. & YOUNG, M. 2010, A Human Factors Approach to Analysing Military Command and Control (http://www.dodccrp.org/events/11th_ICCRTS/html/papers/022.pdf). WEICK, K. SUTCLIFFE, K. 2005, Organizing and the Process of Sensemaking (Organizational Science, Vol. 16, No. 4, 409-421). WEICK, K. SUTCLIFFE, K. 2007, Managing the Unexpected, Resilient Performance in an Age of Uncertainty, (San Francisco, Ca: John Wiley & Sons, Inc). WENGER, E. 1998, Communities of Practice, Learning, Meaning, and Identity (New York: Cambridge University Press). 18
    • Thank you “I’ll live or die by my own ideas.” Johan Cruyff Presentation created October 2013 by Larry Paul, Peoria Arizona. All references are available as stated. All content is the responsibility of the author. For questions or to inquire how to arrange a consultation or workshop on this topic you can contact me at larry4v4@hotmail.com, subject line; decision/action model. For more information visit the bettersoccermorefun channel on YouTube, http://www.youtube.com/user/bettersoccermorefun?feature=watch or Street soccer, a guide to using small sided games at Udemy, https://www.udemy.com/street-soccer-a-guide-to-using-small-sidedgames/?sl=E0IZeFxSVw%3D%3D 19