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BA 511 Politics & Games. A Sampling of Managerial-Related Issues. Firms as Governance Structures. Coase -Williamson- Radner Aside on transactions costs/nature of firm Markets: Strong incentives & contract dispute Weak administrative control/planning Firms: - PowerPoint PPT Presentation
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BA 511 Politics & Games
A Sampling of A Sampling of
Managerial-Related IssuesManagerial-Related Issues
Firms as Governance Structures
Coase-Williamson-RadnerCoase-Williamson-Radner Aside on transactions costs/nature of firmAside on transactions costs/nature of firm Markets:Markets:
Strong incentives & contract disputeStrong incentives & contract disputeWeak administrative control/planningWeak administrative control/planning
Firms:Firms:Strong on administrative controlStrong on administrative controlWeak on incentives & contract disputeWeak on incentives & contract dispute
Optimal mix make-buy; optimal planning; market in Optimal mix make-buy; optimal planning; market in firm; pre-contract & post-contract issuesfirm; pre-contract & post-contract issues
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Decision Mechanism Problems Case: 1912 ElectionCase: 1912 Election
Wilson 42%; Roosevelt 27%; Taft 23 %; Debs 6%Wilson 42%; Roosevelt 27%; Taft 23 %; Debs 6% LA “David Duke”; 2008 GOP Primaries …LA “David Duke”; 2008 GOP Primaries …
Arrow Theorem: No mechanism (voting; market) can satisfy basic Arrow Theorem: No mechanism (voting; market) can satisfy basic conditions: conditions:
(i) no dictator; (iii) all relevant preferences/options matter; (i) no dictator; (iii) all relevant preferences/options matter;
(iii ) consistency outcomes with order/arrangement; (iii ) consistency outcomes with order/arrangement;
(iv) consistency from individual to aggregate preferences;(iv) consistency from individual to aggregate preferences; AT: violation inevitable; which is most acceptable?AT: violation inevitable; which is most acceptable? Mechanisms subject to manipulationMechanisms subject to manipulation
Management Implications? Management Implications? Proposal order; proposal voting rules; decision chain; Proposal order; proposal voting rules; decision chain;
Costs of Decisions: No Magic in 50% Rule
Percent in Agreement
Costs of additional Parties to agreement
Benefits of additional Parties to agreement
100% 0% M*
Policies (rules) v. Discretion Tradeoffs
Cases: Cases: NWA Detroit Tarmac DebacleNWA Detroit Tarmac Debacle
Coach K: “people set rules to keep from Coach K: “people set rules to keep from making decisions”making decisions”
End-of-year budget bait-switchEnd-of-year budget bait-switch Econ IssuesEcon Issues
How costly is discretion? How costly is discretion? Influence on incentives/forward-looking behaviorInfluence on incentives/forward-looking behavior How widespread/systematic is the event/problemHow widespread/systematic is the event/problem
How costly is adherence to policyHow costly is adherence to policy Specifying contingencies; writing rules (TC)Specifying contingencies; writing rules (TC)
Paradox: Rule for departing from rule?Paradox: Rule for departing from rule?
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Change Politics & Org Inertia
Cases: Cases: Branch Rickey: Brooklyn Dodgers; Branch Rickey: Brooklyn Dodgers; Jack Welch: GE; UTA PresidentJack Welch: GE; UTA President
Basic PrinciplesBasic Principles Optimal rate of changeOptimal rate of change Interest group dynamicsInterest group dynamics Who can you count onWho can you count on
Higher level managementHigher level management““Lieutenants” Lieutenants” 6
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Politics & “Games”
Examples of Settings and Behaviors Examples of Settings and Behaviors Group/individual Cooperation Dynamics (inter/intra group, unit)Group/individual Cooperation Dynamics (inter/intra group, unit)
Prisoner’s Dilemma; Hostage’s DilemmaPrisoner’s Dilemma; Hostage’s Dilemma Promises/Threats/CommitmentsPromises/Threats/Commitments
Cooperation/Non-coop: Prisoner’s Dilemma; Hostage’s DilemmaCooperation/Non-coop: Prisoner’s Dilemma; Hostage’s Dilemma Pricing games; Inter/Intra-unit or group dynamics; Pricing games; Inter/Intra-unit or group dynamics;
Location GamesLocation Games Geographic; ProposalGeographic; Proposal
Bidding-Bargaining; Bidding-Bargaining; Employment Processes (hiring; monitoring)Employment Processes (hiring; monitoring)
Signalling-FilteringSignalling-Filtering MiscMisc
Pricing, Ads, Competition, Entry, ExitPricing, Ads, Competition, Entry, Exit Parent-child; Spouses; SiblingsParent-child; Spouses; Siblings
Many governance (and market) settings involve strategizing of 2 (or more) players where their decisions are “actively interdependent”
• Not just “playing against market” with fixed prices/behavior • Think chess, poker, or rock-paper-scissors, not roulette
Essential Features of Games
Case: Consider Poker or “Chicken”Case: Consider Poker or “Chicken” PlayersPlayers Information/beliefs of playersInformation/beliefs of players Actions/strategies available to playersActions/strategies available to players Payoffs to moves/pathsPayoffs to moves/paths Timing of moves/sequence or togetherTiming of moves/sequence or together Manipulation of any of these elements by Manipulation of any of these elements by
playersplayers Solving: Putting self in other’s shoesSolving: Putting self in other’s shoes
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Solution Concepts & Game Basics:Thinking Ahead—Reasoning Back Case: Apollo 1 AftermathCase: Apollo 1 Aftermath
Tom Hanks’ HBO series-- Tom Hanks’ HBO series-- From the Earth to the MoonFrom the Earth to the Moon– – U.S. space program from Mercury through moon landings. U.S. space program from Mercury through moon landings. Strategic segment involves the aftermath of the Apollo 1 deaths of Strategic segment involves the aftermath of the Apollo 1 deaths of 3 astronauts during a launch pad tests:3 astronauts during a launch pad tests:
A capsule fire during a routine test. A capsule fire during a routine test. The fire resulted from a spark in wiringThe fire resulted from a spark in wiring The test under highly pressurized, pure oxygen capsule; contractor The test under highly pressurized, pure oxygen capsule; contractor
(North American) sent repeated warnings to NASA (North American) sent repeated warnings to NASA Capsule reached temperatures over 1000 in 15 secondsCapsule reached temperatures over 1000 in 15 seconds NASA planned to lay substantial blame on NA NASA planned to lay substantial blame on NA NA chief engineer argues for exposing NASA memosNA chief engineer argues for exposing NASA memos NA executive, “no, we’re not and goes on to respond; we’re going NA executive, “no, we’re not and goes on to respond; we’re going
to just take it”to just take it” Can you make sense of the boss’ decision?Can you make sense of the boss’ decision?
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Manipulating Elements of the Game and Countering Manipulation Cases: Cortez ship burning; Poker bettingCases: Cortez ship burning; Poker betting
(Credible) Commitment & actions/beliefs (Credible) Commitment & actions/beliefs
Cases: Retail Stores; My DaughterCases: Retail Stores; My Daughter Limiting available actionsLimiting available actions Expanding available actionsExpanding available actions
Case: Proposal Consideration by Committees/Task Forces Case: Proposal Consideration by Committees/Task Forces Agenda ControlAgenda Control Amendments Amendments
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First or Second Mover Advantage?
Case: Princess BrideCase: Princess Bride First Mover Advantage if manipulation of First Mover Advantage if manipulation of
possible through changing game or beliefs of possible through changing game or beliefs of rivalrival
Cases: Sailing; NCAA Football OvertimeCases: Sailing; NCAA Football Overtime Second Mover Advantage if information Second Mover Advantage if information
becomes available by rival’s move becomes available by rival’s move Poker?Poker?
Tradeoff: info manipulation v. info gatheringTradeoff: info manipulation v. info gathering
Smart but Not Too Smart
Case: Email Phishing SchemesCase: Email Phishing Schemes Too good to be true, probably isToo good to be true, probably is
Case: Colts in 2010 Super BowlCase: Colts in 2010 Super Bowl Dominant strategy but a lot of mixingDominant strategy but a lot of mixing Don’t outsmart yourselfDon’t outsmart yourself
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“Location” Games (with extensive information and single dimension) Where to setup shop if consumer/voters positioned Where to setup shop if consumer/voters positioned
uniformly (or normally) along a road, given that uniformly (or normally) along a road, given that competitor is trying to setup shop in best location also?competitor is trying to setup shop in best location also?
Simple Solution: Move to the middle (median), Simple Solution: Move to the middle (median), otherwise, competitor can locate just to the “busier” otherwise, competitor can locate just to the “busier” side and capture everyone on that sideside and capture everyone on that side
Examples: Variety of retail stores; primary & general Examples: Variety of retail stores; primary & general election races; election races;
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“Location” Games(with very limited information) Where to setup shop if consumer/voters clustered in 10 large Where to setup shop if consumer/voters clustered in 10 large
cities, you will locate in 5 and a rival firm will locate in 5 but cities, you will locate in 5 and a rival firm will locate in 5 but agreements that divide cities are strictly prohibited and agreements that divide cities are strictly prohibited and punishable by large punitive fines? punishable by large punitive fines?
Solutions: use of “focal points”Solutions: use of “focal points”
Stanford-Harvard MBA “Divide the Cities” Game Stanford-Harvard MBA “Divide the Cities” Game
Variants: T. Schelling (Strategy of Conflict) where to meet Variants: T. Schelling (Strategy of Conflict) where to meet in NYC? in NYC?
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Median Location/Voter Model Breakdown(multiple dimensions)
Customer /Voter Preferences - Metrics
No single dominant position due to asymmetry of preferences
X: Traffic0
Y: Income
Prisoner’s Dilemma Criminals arrested; interrogated w/no cooperationCriminals arrested; interrogated w/no cooperation
Choices: Confess/Don’t confessChoices: Confess/Don’t confess1 Confesses = low/high sentences1 Confesses = low/high sentences2 Confess = moderate sentences2 Confess = moderate sentences0 Confess = low/acquittal0 Confess = low/acquittal
Outcomes: cooperative (positive sum) solutionOutcomes: cooperative (positive sum) solution
v. competitive solutionv. competitive solution How to arrive at cooperative solution?How to arrive at cooperative solution?
With no/limited info & explicit cooperation?With no/limited info & explicit cooperation? With cheating?With cheating?
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Prisoner’s Dilemma-like Games
Hostage’s DilemmaHostage’s Dilemma Multi-person version of PDMulti-person version of PD
Oligopoly Games Oligopoly Games (pricing, ads, entry, …)(pricing, ads, entry, …) Cooperation (maybe implicit) leads to Cooperation (maybe implicit) leads to
higher profits than competitionhigher profits than competition
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Game Basics: Bargaining Tactics
Case: Builder-HomeownerCase: Builder-Homeowner Understanding Incentives/Tradeoffs & InfoUnderstanding Incentives/Tradeoffs & Info
Info asymmetry Info asymmetry Flex-price: flexibility of changes; no “hold-out problem”; Flex-price: flexibility of changes; no “hold-out problem”;
wrong incentive for info problemwrong incentive for info problem Fixed-price: Incentive to monitor & control expenses; Fixed-price: Incentive to monitor & control expenses;
“hold-out” problem on changes; incentive to cut corners“hold-out” problem on changes; incentive to cut corners Case: Car-buyingCase: Car-buying
Signaling Signaling Patience is best signal of patiencePatience is best signal of patience
Ultimatum Game (and related) theory and experimentationUltimatum Game (and related) theory and experimentation Split of pot if 2 parties agree on split; 1 makes offer-1 Split of pot if 2 parties agree on split; 1 makes offer-1
accepts or declines offer; accepts or declines offer; Variations: size of pot; depreciation of pot; anonymity; Variations: size of pot; depreciation of pot; anonymity;
repetition; wealth of participants; …repetition; wealth of participants; … Money matters but not all that mattersMoney matters but not all that matters Typical outcomes: bigger than 99:1, less than 50:50Typical outcomes: bigger than 99:1, less than 50:50 Patience is a virtue Patience is a virtue Patience is the best signal of patiencePatience is the best signal of patience Tradeoffs in most bargaining situationsTradeoffs in most bargaining situations
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Bargaining Tradeoffs(Home Building-Purchasing Case) Builder-HomeownerBuilder-Homeowner
Builder info advantageBuilder info advantage Buyer Choices: Buyer Choices:
Flex Price w/fixed percentageFlex Price w/fixed percentageFixed-Price w/negotiated changesFixed-Price w/negotiated changes
Info/Incentive TradeoffsInfo/Incentive Tradeoffs
Insight on Solutions ““Nash Equilibrium”: outcome where Nash Equilibrium”: outcome where
opponent doing best possible opponent doing best possible Sequential Sequential
““Rollback”: Look ahead to last period Rollback”: Look ahead to last period and work backand work back
Simultaneous Simultaneous Iterative: step-by-step analysis of best Iterative: step-by-step analysis of best
choice given a decision by otherchoice given a decision by other Repeated SimultaneousRepeated Simultaneous
Rollback + IterativeRollback + Iterative 21
Iterative Solutions to Simultaneous Game(PD Example)
• Payoffs = (Coke profits , Pepsi profits)
• Decisions: Price Low or Price High
Pepsi DecisionPepsi Decision
LowLow High High
CokeCoke
DecisionDecision
LowLow 1010,10,10 11,,2020
HighHigh 2020,,11 33,,33
Solutions to Simultaneous
• First Iteration: Coke considers best choice if Pepsi sets low price (column 1)
Pepsi Pepsi
LowLow
Coke Coke LowLow 1010,10,10
HighHigh 2020,,11
Best choice for Coke, if Pepsi Sets Low Price
Solutions to Simultaneous
• Second Iteration: Coke considers best choice if Pepsi sets high price;
• Low is dominant strategy for Coke; Low better than high in both iterations
PepsiPepsi
HighHigh
CokeCokeLowLow 11,,2020
HighHigh 33,,33
Best outcome for Coke, If Pepsi Sets High Price
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Solving Sequential Games
““Life must be understood backward, but … it must be lived Life must be understood backward, but … it must be lived forward.” - forward.” - Soren KierkegaardSoren Kierkegaard
(Consider Chess as Example)(Consider Chess as Example)
Diagram a game tree – simplify if needed Diagram a game tree – simplify if needed Start with the last move in the gameStart with the last move in the game Determine the best course(s) of action for the Determine the best course(s) of action for the
player with the last move player with the last move Trim the tree -- Eliminate the dominated Trim the tree -- Eliminate the dominated
strategiesstrategies Repeat the procedure at the prior decision Repeat the procedure at the prior decision
node(s) with the trimmed treenode(s) with the trimmed tree
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An Example: Market Entry
Game Essentials:Game Essentials: Players: Current firm (F) with large market Players: Current firm (F) with large market
share faces a potential entrant (E)share faces a potential entrant (E) Timing: Potential entrant moves firstTiming: Potential entrant moves first Moves: Potential entrant (enter-stay out) Moves: Potential entrant (enter-stay out)
Current firm (accept passively-fight)Current firm (accept passively-fight) Information: full informationInformation: full information Payoffs: (see game tree)Payoffs: (see game tree) Rules: Fixed (to simplify game for now)Rules: Fixed (to simplify game for now)
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Scenario in Game Tree
E out
inF fight
acc
(0, 100)
(-10,-20)
(20,75)
Payoffs = (E, F) expressed as profits (mil $)
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Looking Forward…
Entrant makes the first move:Entrant makes the first move:Must consider how F will respondMust consider how F will respond
If enter:If enter:
Current Firm better off if accepts; so trim “fight” Current Firm better off if accepts; so trim “fight” branch from treebranch from tree
F fight
acc
(-10,-20)
(20,75)
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Now consider entrant’s move with tree trimmedNow consider entrant’s move with tree trimmed
Solution = (In, Solution = (In, Accept Passively Accept Passively ))
… And Reasoning Back
E out
in F
(0, 100)
(20,75)acc
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