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