Teams must coordinate without verbal communication. Round 1: Each table silently chooses a number between 1-100, trying to match the average of all tables. Round 2: Same task, but now knowing Round 1 results. Round 3: Teams try to all pick the SAME number. Debrief: What made coordination hard? Easy? How did common knowledge (knowing that others know...) help in Round 3?

Each table is a 'player' in a multi-round PD tournament. Each round, tables simultaneously vote to Cooperate or Defect. Payoffs: Both C = 3 points each, Both D = 1 point each, One defects = 5 points (defector) and 0 points (cooperator). Run 5 rounds with visible leaderboard. Include one 'final round' announcement, then one surprise 'actually one more' round. Debrief: What strategies emerged? How did knowing it was the last round change behavior?

Project scenarios one at a time (e.g., 'Choosing breakfast cereal,' 'Bidding in an auction,' 'Deciding whether to study,' 'Timing your arrival to avoid traffic'). Tables have 15 seconds to discuss, then hold up 'Strategic' or 'Solo' cards. Award points for correct classifications with brief justifications. Tricky cases spark debate and reveal the boundaries of strategic interaction.

Assign each table a different historical figure/event (von Neumann, Nash, Schelling, RAND Corporation, Prisoner's Dilemma origin, evolutionary game theory). Give 5 minutes to read a one-page summary. Then do 'expert exchanges': one person from each table rotates to another table to share their story (2 minutes each). After rotations, tables piece together the full timeline. Conclude with a projected timeline showing how ideas built on each other.

Present a strategic scenario (e.g., 'Two coffee shops choosing locations on a street'). Each table secretly writes their strategy choice AND their prediction of what most other tables will choose. Reveal all choices simultaneously. Award points for correct predictions. Run 2-3 rounds with different scenarios. Debrief: When were predictions accurate? What makes other players' choices predictable (or not)?

Challenge: Each table creates a real-world scenario that has Prisoner's Dilemma structure. Must identify: the two players, their strategy choices, and the four payoff outcomes that create the dilemma (mutual cooperation is better than mutual defection, but individual defection is tempting). Tables present their best example (1 minute each) to 2-3 neighboring tables in a 'gallery walk' style. Class votes on most creative and most realistic examples.

Each table receives a simple strategic scenario card (e.g., 'Two coffee shops deciding whether to offer free WiFi'). Teams simultaneously: (1) identify the players and their possible actions (3 min), (2) construct the complete payoff matrix on their whiteboard/large paper (5 min), (3) swap matrices with a neighboring table to check each other's work and identify the Nash equilibrium (4 min). Instructor calls on 2-3 tables to explain their reasoning to the full class.

Tables play 3 rounds of a simple simultaneous-move game (e.g., 'Divide 100 Points': each table secretly allocates 100 points between two options; payoffs depend on all tables' choices). After each round: (1) tables submit choices via online form/cards (1 min), (2) instructor reveals aggregate results and calculates payoffs (1 min), (3) tables have 1 minute to discuss and adjust strategy. After all rounds, discuss: What strategies emerged? How did strategies differ from individual actions? What information would have helped?

Half the tables receive a game tree diagram; the other half receive the payoff matrix of the SAME game. Challenge: (1) Teams with trees must construct the normal form matrix (7 min), (2) Teams with matrices must construct the extensive form tree, including information sets (7 min), (3) Partner up with a team that had the opposite form to verify correctness (4 min). Discuss as a class: Which form makes different strategic features more visible?

Each table receives an incomplete game tree missing the information set markings. Tables must: (1) Read the game scenario description carefully to determine what each player knows when they move (3 min), (2) Add information sets to their tree using dotted lines or highlighting (4 min), (3) Exchange trees with another table and explain their reasoning for 2-3 key decision points (3 min). Class discussion: How do information sets change what counts as a 'strategy'?

Each table receives 12-15 cards with statements written on them (mix of strategies, single actions, and outcomes). Challenge: Sort cards into three piles: 'Complete Strategy,' 'Single Action,' and 'Neither/Outcome' (4 min). Tables then defend 2-3 controversial choices to neighboring tables (3 min). Instructor highlights most commonly misclassified examples and leads 2-minute discussion on why the distinction matters.

Each table receives a game tree representing a simultaneous-move game (imperfect information, shown with information sets). Challenge: (1) Redraw the game as a sequential game where player 1 moves first and player 2 observes before moving—perfect information version (6 min), (2) Construct the normal form matrix for BOTH versions (5 min), (3) Compare: How do the strategy sets differ? How do equilibria change? (4 min). Share one key insight with the class (3 min).

Each student privately writes down a number between 0-100. Instructor collects and calculates the average, then multiplies by 2/3. The student(s) whose number is closest to this target wins a small prize. Before revealing: ask tables to discuss what number they'll choose and why. Poll the room to see distribution (show of hands for ranges: 0-10, 11-25, 26-50, 51-75, 76-100). Then reveal the winning number and debrief: What's the dominated strategy? What survives iterated elimination? What's the Nash equilibrium? Most students pick 20-40 first time, demonstrating limited depth of reasoning.

Pair up tables (2 tables = 1 match). Each table is a 'player' that must collectively decide: Cooperate or Defect. Give them the classic payoff matrix (both cooperate: 3,3; both defect: 1,1; one defects: 5,0). Each table has 2 minutes to discuss strategy privately. Simultaneously reveal choices using colored cards (green=cooperate, red=defect). Play 3-5 rounds. Debrief: Did tables play the dominant strategy? Why or why not? What happened over multiple rounds? Introduce the concept that defection is strictly dominant, yet cooperation is mutually better—this is the tragedy of Nash equilibrium.

Give each table two cards: RED and BLUE. Explain payoffs: If your table and your 'partner table' both choose RED: 10 points each. Both choose BLUE: 10 points each. Mismatch: 0 points for both. Randomly assign partner tables (announce via projection: 'Table 1 partners with Table 8'). No communication allowed between partner tables. Tables have 90 seconds to choose. Reveal simultaneously. Most will fail to coordinate in round 1. Round 2: Allow 30 seconds of shouted signals. Did coordination improve? Debrief: This game has TWO Nash equilibria (both RED, both BLUE). How do players coordinate when both strategies are equally good? Introduce focal points and the coordination problem.

Project a 3x3 payoff matrix on screen. Each table receives a printed copy. Challenge: 'Circle all best responses as fast as you can, then identify all Nash equilibria.' This is a race—first table to correctly identify all NE wins. After winner declared, walk through as class: For Player 1, underline best response to each of Player 2's strategies. For Player 2, do same for columns. Nash equilibria are where both players are best-responding (marked cells have both underlines). Use a simple game with 2-3 Nash equilibria. Repeat with a second, trickier matrix. Debrief: Best response analysis is the systematic way to find Nash equilibria.

Each student chooses: STAG or HARE. Payoffs: Both hunt Stag (requires coordination): 4 points each. One hunts Stag, other hunts Hare: Stag hunter gets 0, Hare hunter gets 3. Both hunt Hare: 3 points each. Pair students at their tables. Round 1: No communication, write down choice. Reveal and calculate points. Round 2: Same pairing, but 30-second discussion allowed. Survey: Who switched strategies? Debrief: (Stag, Stag) and (Hare, Hare) are both Nash equilibria, but (Stag, Stag) is better. Why don't people always coordinate there? Introduce concepts: risk dominance vs. payoff dominance. Hare is 'safer' but Stag is better if you trust others.

Project 4-5 different game matrices sequentially (5x4, 3x3 variations). For each matrix, tables have 60 seconds to identify: (1) Any strictly dominated strategies, (2) Any weakly dominated strategies, (3) Write answers on whiteboard/paper. After time's up, cold-call tables for answers. Award points for correct identifications. After 3-4 games, announce winner. Final challenge: Show a game where iterated elimination works—can they find the Nash equilibrium by eliminating dominated strategies round by round? Debrief: When is dominance useful for predicting play? What survives iterated elimination?

Each table splits into two groups. Group A writes down what they think is a random sequence of 20 L's and R's (left/right). Group B then tries to predict the next choice in the sequence by looking for patterns humans unconsciously create (alternation bias, avoiding long runs, etc.). Groups swap roles and compare prediction accuracy. Discuss why humans are terrible random number generators and why this matters for mixed strategies.

Within each table, pairs play a simplified penalty kick game (kicker: left/right, goalie: left/right). If choices match, goalie saves (goalie +1, kicker -1); if different, goal (kicker +1, goalie -1). Round 1: 10 kicks, play intuitively. Round 2: Calculate the Nash equilibrium mixed strategy (50/50). Round 3: 10 more kicks trying to implement the mix. Tables discuss: Did anyone deviate from 50/50? Did anyone get exploited? What happened to win rates?

Tables play modified RPS tournament. Round 1: Standard RPS (winner gets 1 point). Round 2: Rock wins get 3 points, Paper/Scissors wins get 1 point. Students predict: How should strategy change? Play 15 rounds and track frequencies. Round 3: Calculate expected utility for different mixing probabilities against the observed distribution. Did the equilibrium shift? Why should Rock now be played less often (not more)?

Each table plays 'Matching Pennies' in teams (Team A wants to match, Team B wants to mismatch). Twist: After every 5 rounds, teams huddle to decide if they see patterns in opponents' play and adjust strategy. Teams must simultaneously reveal strategies. Then play 5 more rounds. Can teams exploit each other, or does awareness force convergence to 50/50? Debrief: When is randomization essential vs. when can you exploit non-equilibrium play?

Instructor provides each table with a 2x2 game matrix (asymmetric, no pure Nash). Tables have 7 minutes to: (1) Calculate the mixed strategy Nash equilibrium using the indifference equations, (2) Calculate expected utility at equilibrium. Then tables pair up and play 20 rounds against another table. Did actual play approximate the equilibrium? Which table came closest? Were payoffs near the predicted expected utility?

Quick empirical test: Each table member simultaneously reveals a choice (A or B) for 15 rounds, trying to be 'maximally random.' Then analyze: (1) Was overall frequency 50/50? (2) Count streaks (AAA, BBB). (3) Calculate how often choices switched vs. repeated. Compare to what true randomness would generate (binomial distribution). Discuss: If opponents track these patterns, how could you be exploited? This reveals why genuine mixing requires external randomization (coins, dice).

Each table splits into proposers and responders. Round 1: Proposers write down their offer (split of $100) on paper, responders simultaneously write accept/reject threshold. Collect data live via quick poll or show of hands. Display aggregate results on screen. Round 2: Tables discuss predictions using backward induction - what SHOULD rational actors do? Round 3: Run again with new roles, compare actual behavior vs. theoretical prediction. Debrief on why people deviate from subgame perfect equilibrium.

Present 3 business scenarios (e.g., 'CEO threatens to shut down division if workers strike', 'Firm threatens price war if competitor enters market', 'Parent company threatens to pull funding from subsidiary'). Each table acts as a 'credibility court' - they have 3 minutes per scenario to determine if threat is credible using backward induction. Tables hold up color cards: GREEN (credible), RED (non-credible), YELLOW (depends). Randomly call on tables to defend their verdict. Score points for correct reasoning, not just correct answer.

Project a game tree on screen (start simple, get complex). Tables race to solve via backward induction. First table to correctly identify the subgame perfect equilibrium path rings bell/raises hand. They explain their reasoning; if correct, they get point. Run 4-5 increasingly complex trees. Include at least one trick tree where the Nash equilibrium differs from subgame perfect equilibrium. Winning table gets to challenge instructor to solve one THEY create.

Tables pair up to play multi-round centipede game. Each table designates Player 1 and Player 2. Start with small pot ($4), double each round. Players alternate taking or passing. TWIST: Use a 'betrayal scoreboard' - track which tables cooperate longest before someone takes. After 3 pairings, discuss: Did first-mover advantage matter? When did people take vs. theory prediction (immediately)? Did reputation across rounds change behavior? Reveal that longest-cooperating table wins bonus point.

Present a sequential market entry scenario: Two firms can enter a market, but first mover gets cost advantage. Each table splits into Firm A (decides first) and Firm B (observes then decides). Firm A writes 'Enter' or 'Stay Out' privately. Then reveal simultaneously. Calculate payoffs based on game tree logic. Rotate roles and change parameters (e.g., vary first-mover advantage size). Tables track when first-mover advantage matters vs. doesn't. Debrief: When is moving first valuable? When is it better to wait and observe?

Show 3-4 proposed strategies for sequential games that contain non-credible threats or backwards induction errors. Example: 'Player 2 will reject any offer below $80 in the ultimatum game' or 'We'll flood the market if competitor enters, even though we'd lose money'. Tables have 2 minutes per case to: (1) identify why strategy fails backward induction, (2) write a credible alternative. Cold-call tables for explanations. Vote on best 'fixed' strategy.

Each table is a team. Teams simultaneously play iterated Prisoner's Dilemma against other tables (instructor projects matchups on screen). Round 1: 5 iterations against Table A. Round 2: 5 iterations against Table B. Teams record their strategy choices on whiteboards and hold them up simultaneously each round. After each pairing, teams briefly discuss what happened and adjust their strategy. Instructor tracks cooperation rates on a live scoreboard. Debrief: Which teams cooperated most? Did your strategy change based on your opponent's reputation?

Teams have 5 minutes to design a repeated-game strategy (not just Tit-for-Tat — be creative!). Write it as a simple algorithm on a poster: 'IF opponent did X, THEN we do Y.' Teams present their strategy (1 min each, 2-3 teams volunteer or are randomly selected). The class votes on which strategy they predict will perform best in a 10-round game against various opponents. Instructor reveals that these are variations of actual submitted strategies from Axelrod's tournament. Discuss why complexity doesn't always win.

Instructor presents a repeated game scenario. Each table receives a 'discount factor' card (ranging from δ=0.3 to δ=0.95). Tables calculate whether cooperation is sustainable in their assigned world. Tables physically arrange themselves along a spectrum in the room from 'Cooperation Impossible' to 'Cooperation Easy' based on their calculations. Teams at different points debate: why do those with δ=0.9 cooperate when you with δ=0.4 can't? Instructor connects to real-world: which environments/situations have high vs. low discount factors?

Instructor sets up a 3-round scenario where 'Country A' (one volunteer) plays against 'Country B' (another volunteer) with the class as advisors. Country A commits to grim trigger. In Round 2, Country B 'accidentally' defects (instructor reveals it was a misunderstanding/error, not intentional). Pause the game. Tables debate for 3 minutes: Should Country A forgive or trigger eternal punishment? Each table votes and justifies. Execute the majority decision and play out the remaining rounds. Compare outcomes with the road not taken. Discuss commitment vs. flexibility.

Present a 2x2 game with a Pareto-dominated outcome (e.g., both players getting low payoffs) and challenge tables: 'The folk theorem says ANY individually rational outcome can be sustained in equilibrium. Design a strategy profile that keeps us stuck at this terrible outcome forever.' Tables have 7 minutes to design punishment schemes that make deviation unprofitable. Gallery walk: half the tables stay to explain their mechanism, half rotate to hear others' designs. Reconvene to vote on most creative/robust mechanism. Debrief: If we can sustain bad outcomes, what's the coordination problem in reaching good ones?

Each table starts with 10 'reputation points.' Tables simultaneously decide whether to 'cooperate' (invest 2 points) or 'defect' (invest 0) in a public goods game each round. Cooperating tables earn returns based on total cooperation (the more who cooperate, the higher the multiplier). BUT tables can spend 3 reputation points to 'investigate' another table's history and make it public. After 3 rounds, tables with highest combined wealth AND reputation win. Debrief: Did reputation matter more than immediate payoffs? When did you spend points to signal or investigate?

Each table is randomly assigned 'seller' or 'buyer' role. Sellers secretly draw a card indicating if their car is a 'peach' (high quality) or 'lemon' (low quality). Round 1: Sellers can only state a price; no other communication allowed. Buyers must decide whether to buy. Round 2: Sellers can now make ANY claims about their car. Round 3: Introduce costly inspection option for buyers ($50 fee to see the card). After each round, calculate market outcomes and discuss why pooling happens, why the market might collapse, and how screening mechanisms help.

One student per table is secretly designated 'the spy' (knows a hidden number 1-100). Tables must identify their spy through questioning, but the spy can lie. Before questioning starts, what's the prior probability for each person? After each answer, tables must UPDATE their beliefs using Bayesian logic and track probability assignments on their board. After 5 questions, teams make their final accusation. Reveal spies and calculate which teams used evidence most effectively. Debrief: How did you weight contradictory signals? When did you dismiss vs. incorporate lies?

Within each table, randomly assign students as 'high ability' or 'low ability' workers (private information). High ability workers produce $100 value, low ability $40. All workers can purchase 'education' (0-4 years) at differential costs: $15/year for high ability, $35/year for low ability. Employers (instructor or designated tables) announce wages based on education level only. Round 1: Workers choose education levels simultaneously, employers set wage schedule. Round 2: Employers adjust wages after seeing Round 1 choices. Do separating or pooling equilibria emerge? Which workers are better off? Is education socially wasteful?

Pair up tables (2 tables work together). One table represents a seller who knows their product quality (high/medium/low - draw randomly). Other table represents buyers. Sellers can make ANY claims about quality but cannot provide proof. Buyers must decide how much to pay (winning bid closest to true value gets points). After 3 rounds, rotate partners. Key insight: When are cheap talk claims credible? Students discover that only when interests align (e.g., repeated interaction, reputation concerns) does cheap talk gain credibility. Debrief strategic communication vs. verifiable information.

Teams are insurance companies who must design menu of contracts to screen customers into 'high risk' and 'low risk' without knowing their type. Each team designs 2-3 contract options (coverage level + premium). Then teammates secretly draw risk-type cards and choose which contract they'd select. Did the menu successfully separate types? Did low-risk subsidize high-risk (pooling)? Teams present their menus, compare separation success rates, discuss what made effective screening mechanisms. Introduce adverse selection death spiral scenario where low-risk exit entirely.

Present a real-world scenario (e.g., college admissions, restaurant health ratings, startup funding). Tables must predict: Will this market reach pooling or separating equilibrium? Why? Each table stakes reputation points on their prediction and must justify with game theory logic. Introduce perturbations (e.g., 'cost of signal drops by 50%' or 'regulation bans certain signals'). How does equilibrium change? Tables update predictions in real-time. Award points for correct predictions AND quality of reasoning. Creates debate about what conditions favor each equilibrium type.

Run four simultaneous mini-auctions for identical items (e.g., $5 gift cards), each using a different format. Assign 2-3 tables to each format. Round 1: English auction (open ascending). Round 2: Dutch auction (descending clock). Round 3: First-price sealed-bid. Round 4: Vickrey (second-price sealed-bid). Teams pool their hypothetical budgets and submit one bid per team. After all four rounds, debrief: Which format generated highest revenue? How did bidding behavior differ? Did teams bid differently when they could see others vs. sealed? Connect to revenue equivalence theorem and why it might break down in practice.

Display a jar filled with pennies (or show an image). Each table estimates the value and submits a sealed bid. The highest bidder wins but pays their bid and receives the actual value. Reveal the true value. The 'winner' likely overbid (winner's curse). Discuss: Why does the winner typically lose money? Connect to common value auctions, oil drilling rights, and spectrum auctions. Key insight: You win only when you've overbid everyone else, which suggests you've probably overestimated. Tables then revise their bids accounting for winner's curse and resubmit. Compare first-round vs. second-round bids to show learning.

Give each table a secret private valuation for a hypothetical item (values vary across tables: $10, $15, $20, etc.). Run a Vickrey auction where teams submit sealed bids. Reveal all bids and explain: highest bidder wins but pays the second-highest price. Ask: Did anyone bid differently than their true valuation? Run a second round encouraging strategic deviations. Show that bidding your true value is dominant: overbidding risks overpaying without increasing win probability; underbidding risks losing when you would have won profitably. Calculate counterfactuals on board to prove this. Contrast with first-price auction where strategic shading is optimal.

Present a realistic scenario: A government wants to auction 3 regional telecom licenses. Teams are competing telecom companies with different synergies (e.g., 'you already own the north license, so the south license is worth more to you'). Give each table a valuation sheet showing their values for different license combinations. Phase 1 (8 min): Tables strategize their bidding approach. Phase 2 (6 min): Run a simplified simultaneous ascending auction where tables can bid on any license. Phase 3 (4 min): Debrief the outcome. Who won what? Was it efficient? Did anyone face exposure problems (bid on complementary licenses but won only one)? Connect to real FCC spectrum auctions and mechanism design challenges.

Assign each table a specific auction format to defend (English, Dutch, first-price, Vickrey). Give a context: 'A city wants to auction a food truck permit.' Each table gets 3 minutes to prepare arguments for why their format is best for this scenario considering: revenue maximization, efficiency (highest-value bidder wins), simplicity, and fraud resistance. Run 2-minute pitches from 4 selected tables, followed by a class vote on most convincing argument. Instructor then reveals theoretical insights: revenue equivalence under certain assumptions, but practical considerations matter (entry costs, collusion risks, bidder sophistication). Create tension between theory and practice.

Test the revenue equivalence theorem empirically. Run two simultaneous auctions for identical prizes: half the tables participate in a first-price sealed-bid auction, half in a Vickrey auction. Give everyone the same budget. Collect all bids and reveal: winning bids and average revenue from each format. Theory predicts equal expected revenue, but do results match? Discuss deviations: risk aversion (underbidding in Vickrey, over-shading in first-price), joy of winning, spite, or confusion. This shows both the power of theory and the importance of behavioral factors. Run a second round after discussing theory and observe if behavior changes.

Each table represents a 'population' adopting either Hawk or Dove strategy (or mixed). Round 1: Tables announce their strategy. Round 2: Instructor randomly pairs tables to 'compete' for resources. Teams calculate payoffs using the classic Hawk-Dove matrix (Hawk vs Hawk = 0, Hawk vs Dove = 3, Dove vs Hawk = 1, Dove vs Dove = 2). Round 3: Based on cumulative payoffs, teams can 'evolve' — switch strategies if they want. Round 4: Repeat pairings and calculations. Round 5: Class discusses which strategy dominated and why (introduces ESS concept). Instructor reveals that mixed strategy or conditional play often emerges as stable.

Assign each table a different strategy for iterated prisoner's dilemma: Table 1 = Always Cooperate, Table 2 = Always Defect, Table 3 = Tit-for-Tat, Table 4 = Random, Table 5 = Grudger (cooperate until betrayed once, then defect forever), etc. Each table plays 5 rounds against 2-3 other tables, tracking total points (Cooperate/Cooperate = 3/3, Cooperate/Defect = 0/5, Defect/Defect = 1/1). After all matches, tables report total scores. Instructor plots scores on board. Tit-for-Tat typically wins. Class discusses why: it's 'nice, retaliatory, forgiving, clear.' Connect to reciprocal altruism in vampire bats, sticklebacks, and grooming in primates.

Present 5 scenarios on slides where animals must decide whether to help relatives at personal cost. Each table gets a worksheet with scenarios like: 'Ground squirrel alarm calls attract predators (cost = -0.3 fitness). Siblings nearby = 4, relatedness = 0.5, each benefits +0.2 from warning. Does Hamilton's rule (rB > C) predict helping?' Tables race to calculate and vote. After each scenario, reveal answer and briefly explain. Include mix of obvious (full siblings high benefit) and tricky cases (cousins, low benefit). Final scenario is a twist: helping non-kin who help back (introduces reciprocal altruism contrast).

Instructor presents 4 'resident' strategies with payoff structures. Each table is assigned one strategy to test for ESS status. Tables must: (1) Define a plausible 'mutant' strategy, (2) Calculate payoffs when mutant is rare in population of residents, (3) Determine if mutant can invade (earns higher payoff than resident against resident), (4) Prepare 2-minute presentation. Tables then present findings. Class votes on whether each strategy is ESS. Instructor facilitates discussion of what makes something evolutionarily stable (must earn higher payoff against itself than any mutant does against it).

Set up three 'zones' in room: Hawk Zone, Dove Zone, Mixed Strategy Zone. All students start in Mixed (representing initial population distribution). Instructor runs 3 'generations': In each generation, students at their tables calculate expected payoffs for their current strategy given current population frequencies (count heads in each zone). After calculation, students physically move to zone of strategy that performed best. Watch population distribution shift in real-time. Often see oscillations or convergence to mixed strategy equilibrium. Debrief: This is replicator dynamics—frequencies change based on relative fitness. Connect to classic examples like male beetles with different mating strategies.

Show 3 short video clips of animal behavior (e.g., dung beetle fights, baboon grooming, bee waggle dance, lioness cooperative hunting). After each clip, tables have 3 minutes to: (1) Identify what game-theoretic situation this represents (Hawk-Dove? Prisoner's Dilemma? Coordination game?), (2) Predict the likely ESS or evolutionary outcome, (3) Explain what keeps 'cheaters' from dominating. Each table holds up colored card with their answer (A=Hawk-Dove, B=Reciprocal Altruism, C=Kin Selection, D=Other). Instructor polls class, reveals correct interpretation, discusses the mechanisms maintaining cooperation or mixed strategies in each case.

Within each table, students pair up. Each pair negotiates to split 100 points using alternating offers. The twist: after each round of back-and-forth offers (30 seconds each), the total pie shrinks by 20%. If they haven't agreed after 5 rounds, both get zero. After playing, pairs share with their table: When did they settle? Who proposed the deal? Was it close to the theoretical prediction? Instructor then reveals the subgame perfect equilibrium and compares to actual outcomes.

Each table receives 3 real-world bargaining scenarios (e.g., salary negotiation, used car sale, business partnership). For each scenario, teams have 3 minutes to: (1) identify all possible outside options for both parties, (2) rank which party has stronger BATNA, (3) predict the settlement range. Teams write predictions on large post-its. Then, tables swap post-its and critique each other's analysis. Instructor facilitates quick whole-class debrief: Which BATNAs were hidden? How does BATNA strength shift the bargaining range?

Each table splits into sellers and buyers negotiating the sale of a 'company.' Sellers receive a card showing the company's true value (varies by table: $50K, $75K, or $100K). Buyers only know it's worth between $40K-$100K. They have 5 minutes to negotiate a price. After Round 1, debrief: How many deals? Then Round 2: everyone knows all possible values but not which specific value their table has. Compare deal rates. Tables discuss: Why did some negotiations fail? How did uncertainty change offers? Connects to the 'lemons problem' and why information asymmetry causes bargaining failure.

Tables form pairs again. Each negotiator draws a 'patience card' (high patience = 0.95 discount factor, medium = 0.75, low = 0.50). They can't show their card to their partner. They negotiate to split $100, but must calculate their own payoff using their discount factor for each round that passes (30-second rounds). After agreements, reveal cards and calculate actual payoffs. Then table discusses: Did the patient person capture more surplus? Did anyone bluff about their patience? How did uncertainty about the other's patience affect strategy?

Each table receives a bargaining problem with specific utility functions and disagreement points (e.g., two partners splitting profits with different risk preferences, labor-management negotiation with asymmetric costs of delay). Teams have 5 minutes to: (1) propose a 'fair' split and justify it, (2) calculate the Nash bargaining solution. Teams post both solutions. Instructor highlights where intuitive fairness diverges from Nash solution. Class votes on which is truly 'fair.' Sparks debate about what fairness means and whether Nash axioms (Pareto efficiency, symmetry, independence of irrelevant alternatives) actually capture justice.

Each table splits into groups of 3. They must split a $120 pie, but here's the twist: any 2-person coalition can take $80 and exclude the third person entirely. Students have 8 minutes to negotiate. Most groups will experience: unstable coalitions (someone always wants to defect to form a new coalition), chaotic re-negotiation, possible breakdown. After time, debrief: Who ended up with what? How many groups deadlocked? Instructor connects this to Shapley value, core solutions, and why bilateral bargaining models fall apart with more players. Demonstrates limits of simple models.

Each table receives three real-world policy options (e.g., environmental policy A, B, or C) with detailed descriptions. First, students individually rank all three options (1st, 2nd, 3rd choice). Then tables aggregate using pairwise majority votes: A vs. B, B vs. C, then C vs. A. Watch as tables discover they have created voting cycles where A beats B, B beats C, but C beats A. Tables then discuss: How can all three pairwise outcomes reflect 'majority will' yet contradict each other? Debrief as whole class about which tables found cycles and why transitive individual preferences produce intransitive collective preferences.

Challenge each table: 'Design the fairest possible voting system for electing a class president from 4 candidates.' Give them 8 minutes to invent and document their system's rules. Tables present their systems (2 min each for 3-4 tables). Instructor then presents Arrow's conditions on the board and walks through each presented system, showing exactly which Arrow condition it violates. If plurality voting: fails IIA when a spoiler enters. If Borda count: fails to satisfy majority criterion. Tables race to see if ANY table avoided all violations (spoiler: none will). Concludes with revelation of Arrow's theorem: this failure was guaranteed.

Three-round voting game. Each table is a political party choosing how to vote on a bill with amendments. Round 1: Everyone votes sincerely based on their table's assigned preferences. Round 2: Tables can see how others voted and revote—watch strategic voting emerge. Round 3: Tables simultaneously submit strategies, knowing others are strategizing. Track how outcomes shift across rounds. Instructor reveals: This demonstrates Gibbard-Satterthwaite—when stakes matter and information exists, sincere voting becomes irrational. Discuss: If everyone votes strategically, is anyone actually representing their true preferences? What does this mean for interpreting election results?

Pick a genuine campus policy issue (e.g., 'How late should the library stay open?'). Each table sends a representative who positions themselves along a spectrum taped on the floor (9pm to 3am). Representatives can see each other's positions. Run a vote: Majority-rules between the two most extreme positions. Loser drops out. Repeat until one position remains. It will be at/near the median. Now let tables strategically reposition knowing how the voting works. Watch as extreme positions pull toward the median or mediocre compromise emerges. Debrief: Why do candidates campaign for the 'center'? What gets lost when extremes are ignored?

Each table gets a different social choice problem: (1) Allocate a shared resource fairly, (2) Discover who values an item most, (3) Form project teams efficiently, (4) Choose a budget split. Tables have 8 minutes to design a mechanism (rules, incentives, procedure) that gets people to reveal true preferences and achieves the desired outcome. Tables visit neighboring tables to test each other's mechanisms: Can you manipulate this system? Does lying benefit you? Report back. Instructor highlights: Which mechanisms were strategy-proof? Which achieved efficiency? This is mechanism design—engineering social systems that work even when people are self-interested.

Present a real-world voting scenario (e.g., Academy Awards, Olympic host city, department chair election). Each table gets 'currency' to bid on which Arrow condition they'd pay to violate. Non-dictatorship costs 10 points to violate (obviously). But what about IIA? Pareto efficiency? Unrestricted domain? Tables must decide: We can have a dictator OR we can have a system where adding a candidate changes outcomes between original candidates. Which is worse? Tables bid, then defend their choices. Reveals: There's no 'right' answer—it depends on what you value. Democracy isn't about perfect systems but about conscious tradeoffs.

Each table receives a country profile card (e.g., 'Industrial Power: High emissions, strong economy' or 'Vulnerable Island Nation: Low emissions, existential threat'). Tables have 3 minutes to review their incentives. Then open negotiation begins—tables can form coalitions, make side deals, or defect. After 10 minutes, all tables simultaneously commit to an emission reduction level (0-100%). Calculate global outcome and reveal which countries 'win' vs. which face climate disaster. Debrief: Why did cooperation fail/succeed? What mechanisms might help?

Round 1: Each table secretly decides to 'Build Weapons' (aggressive) or 'Seek Diplomacy' (cooperative). Reveal simultaneously. Tables that built while others sought diplomacy gain 'dominance points.' Round 2-4: After each reveal, tables see what others did and decide again. Payoff matrix visible on screen. Watch as paranoia spreads and nearly everyone escalates. Final debrief: How did it feel when others defected? Could you have avoided the arms race? Connect to real Cold War history and current nuclear tensions.

Each table is a competing social platform. Students have individual 'user cards' with preferences (e.g., 'I want to be where my friends are' or 'I prefer best features'). Round 1: Platforms pitch their features (2 min prep, 30 sec each). Users vote with feet—physically stand behind chosen platform. Round 2: Platforms see where users went, can adjust strategy. Users can switch (but lose 'switching cost' points). Round 3: Final choice. Count users at each platform. Discuss: Did winner have best features or just got lucky early? How did network effects compound? Why are platforms so prone to winner-take-all?

Scenario: Online ad auction. Each table designs a simple bidding algorithm: 'Always bid X', 'Bid high if competitors are low', 'Randomize', etc. Write it as IF-THEN rules. Instructor runs 5 auction rounds on screen, applying each table's algorithm. Track wins and costs. Then: Groups can revise algorithms based on what they learned. Run 5 more rounds. Debrief: What strategy emerged as dominant? How did algorithms learn/adapt? What happens when algorithms optimize against each other without human oversight?

Assign each table a case: 'Game theory in nuclear strategy', 'Game theory in climate policy', 'Game theory in platform regulation', etc. Half the tables are prosecutors (critique game theory's use here), half are defenders (argue for its value). Give 5 minutes to prep arguments. Then run quick 2-minute 'trials' where one prosecutor table faces one defender table, class votes on winner. Rotate pairings for 2-3 rounds. Debrief: When is game theory illuminating vs. dangerous? What assumptions break down in real life?

There's a shared resource (fishing ground, forest, atmosphere) with 100 units. Each round (4 rounds total), each table secretly decides how many units to extract (0-20). If total extracted ≤ sustainable threshold, resource regenerates. If exceeded, resource degrades permanently. After each round, reveal total extraction and remaining resource. Watch as overconsumption likely collapses the resource. Then: Give tables 3 minutes to negotiate a binding agreement and run 3 more rounds. Compare outcomes. Discuss: What made cooperation hard? What mechanisms helped?