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Policy Evolution within an Organization James H. Hines Sloan School of Business, Massachusetts Institute of Technology Jody Lee House Department of Electrical and Computer Engineering, Oregon Graduate Institute Funded in part by NSF IOC Award#SES-9975942 The Problem System-wide company improvement is difficult because companies are too complex to “solve.” How can we improve organizations in the face of ignorance? A solution? Biological evolution has produced excellent organizations. Can we identify analogs of natural evolution that will help human organizations to likewise excel? Gene:Organism::Policy:Organization Policy: Implicit or explicit Examples Pricing Hiring Capacity Expansion Flywheel sales Synonyms Decision rule Rule of thumb A policy produces A gene produces • A stream of decisions • A stream of proteins • Activity in the firm • Activity in the cell • Changing the policies, changes the organization • Changing the genes changes the organism Where are “evolutionary packets” stored? • Genes are stored on chromosomes in cells • Policies are stored – In written manuals? – In committees? – On computers? – In brains of people Processes Genes vs Mutation Recombination Natural selection and the sex drive survival of the fittest Policies Innovation Inter-personal learning Pointing and pushing mechanisms learning from the fittest Pointing And Pushing Mechanisms Point to successful people Push others to learn from them Examples Promotion and hierarchy Pay scales The best and latest computers In house training? A brief look at sex Grandpa’s strand Papa Grandma’s strand Mama cell chromosome recombination sperm egg you fertilization recombination Recombination is key • Combine parts of fit organisms to create fitter organism • Example: 4-digit number, A > B fitter 8,765 7,999 8,999 Learning is Similar to Biological Recombination Fred Phyllis brain Time 1 policy Phyllis teaching Fred learning Time 2 Why learning is difficult to call to mind • The donor’s idea is well integrated • The rest of the donor’s idea is difficult to recognize as an idea Overview Step 3: Promote Managers Step 2: Evaluate performance of the system dynamics models Step 1: Run system dynamics simulation models, using policies of the managers Step 4: If using teams: Mix managers and reform teams Step 5: Managers learn Step 6: Managers innovate Step 1: Run SD models Decides Correct code Programmers Decides Productivity Writing code correctly Decides Code to write Correct code Programmers Writing code correctly Correct code Programmers Productivity quality WritingCode Productivity Creating bugs Code to write qualityUndiscovered bugs WritingCode Writing code correctly Code to write quality WritingCode Creating DiscoveringBugs bugs Undiscovered bugs DiscoveringBugs BugDiscoveryTime DiscoveringBugs Creating bugs Undiscovered bugs BugDiscoveryTime BugDiscoveryTime Step 1 The Project Model Detail DesiredPeople HireFire Rate Remaining Time <Time> timeTo Change WF People Productivity WorkToDo DueDate Correctly Doing Doing Correctly Done Normal Quality Quality UndiscoveredBugs Anticipated TimeTo IncorrectlyDoing Complete TimeTo Anticipated BugDetecting DueDate Anticipated Change TimeToDetect Production Rate Schedule Bugs <People> <Time> <Productivity> Step 2: Evaluating Performance Fitness function can be based on any variables in the model Variables can be combined using any functional form In the following we use two simple fitness functions Time to ship (LastPossible – Actual) Number of bugs (LinesOfCode – BuggyLines) Step 3: Promoting managers 1. Rank individuals based on relative performance 2. Promote according to rank. Positionnew Positionold * PromotionFactor The promotion algorithm requires specifying the “promotion base”. A promotion base of 2 means •The highest performing manager’s new position is 2 * theOld •The lowest performing manager’s new position is (1/2) * theOld •Everyone else’s promotion is evenly spread out between 2 and 1/2 Step 3 Promotion Algorithm Detail Positionnew Positionold * PromotionFactor PromotionFactor baseValue K 2 * (rank 1) K 1 populationSize 1 Team-based promotion Positioni ,new Positioni ,old * TeamAPromotionFactor Step 4: If using teams mix them up Randomly? Spread out the best? Concentrate the best? Step 5: Learn a) Select a teacher by roulette b) Learn from the teacher by recombination Pos= Manager5 0.5 Position=1.41 Manager3 Position=2 Manager1 Pos=1 Pos=0.71 Manager2 Step 5 Learn p(learn) OLD Learner’s Policy 10 or 0010 10 Teacher’s Policy 32 or 1000 00 Randomly choose a crossover Point, say 2 Randomly choose which part the learner will obtain and which he will retain 0010__ + ____00 0010 00 = 8 OR ____10 + 1000__ 1000 10 = 34 NEW Learner’s Policy 34 or 100 10 Teacher’s Policy 32 or 1000 00 Step 6: Innovate p(innovate) 111111 Before Flip ! 110111 After Learning, no pushing/pointing: Learning Drift Optimal value = 8 Learning, no pushing/pointing Random Consensus Policy 15 10 5 0 0 5 10 Generation 15 20 Learning with Pointing/Pushing Individuals 16 14 Policy 12 10 8 6 4 2 0 0 5 10 Generation 15 20 Next steps Measurement through knowledge elicitation with partner companies Who learns from who and why? How are implicit policies a function of organizational structure? Integrated simulation
