Game Theory Compensation Strategy: Reward the Win

Estimated reading time: 13 minutes

A Yale economics lecture changed how I price executive judgment. I did not expect a classroom exercise about a duel to reshape my pay work, yet one closing section did exactly that. Years later, it still anchors how I approach game theory compensation strategy heading into 2027.

My handwritten notes from that night were a mess: proactive bias in America, underestimating others, overestimating ourselves, control our destiny, distribution of skill, grab the horns, don’t go down swinging, don’t go down.

Those fragments looked disconnected at first. Read together, though, they became a working method for deciding what to reward, how far to differentiate, and when visible action should not be mistaken for good judgment.

The Yale Lecture Behind This Game Theory Compensation Strategy

Professor Ben Polak’s ECON 159 lecture on backward induction closes with two decision errors that quietly distort pay design: overconfidence and a bias toward acting early.

The duel exercise makes the point concrete. Two players start far apart and walk toward each other, each holding a single shot. Accuracy climbs with every step, so waiting improves the odds of hitting. Fire too soon, and you miss, which hands your opponent a free walk to point-blank range. Students still fire early, over and over, because pulling the trigger feels like doing something while holding position feels like doing nothing.

Polak’s diagnosis lands harder than the math. Americans, he argues, are drawn to the idea of being proactive. We like the language of controlling our own destiny. Grabbing the bull by the horns reads as leadership. Then he closes with the line I wrote down and underlined twice: the aim in life is not to go down swinging, it is not to go down.

That sentence reframed the question for me. Plan design should not reward motion because motion looks bold. Instead, it should reward choices that improve the odds of the outcome the business actually needs.

Boards say they want judgment, resilience, and durable value. Yet the scorecard often pays for visible activity. A leader launches ten initiatives, announces dramatic cuts, closes a large deal, or pushes a rapid AI rollout across three functions. Decisive, certainly. Whether any of it improved the company’s position is a separate question, and most plans never ask it.

The lecture itself is worth an hour: Yale Open Courses, ECON 159, Lecture 16 on backward induction.

MorganHR POV: proactivity is not performance. Sometimes the higher-value behavior is waiting, sequencing, testing, or preserving options until the odds move in your favor.

Why Strategic Pay Design Is a Multiplayer Problem in 2027

Pay is never a one-player decision. Boards, executives, employees, candidates, investors, rivals, and regulators all move in response to one another, each chasing different goals, none holding complete information. Planning for 2027 should therefore look less like a spreadsheet exercise and more like a game with several players.

Market data sharpens the point. Payscale’s 2026-2027 Salary Budget Survey, built on 1,266 submissions collected in May and June of 2026, puts expected United States base pay increases at 3.5% for 2027, up from an actual 3.4% in 2026. More telling is the distribution question. Across-the-board increases reached 36% of organizations in 2026, while only 32% plan them for 2027.

Flat budgets plus shrinking appetite for uniform treatment means differentiation is no longer a philosophy debate. It has become a rationing decision. So a game theory compensation strategy starts by asking which outcomes matter, who can move them, and what rivals will do next.

AI raises the stakes further. The question is not whether to hire more technical talent. Leaders have to decide which roles now create scarce value because the people in them convert new tools into better products, lower costs, or faster decisions. Role value can move faster than the annual market-pricing cycle can track it.

I am watching this play out with a client that formed a two-to-three-year external team to identify where AI belongs in their operating playbook. That move is rational. Less rational is how rarely internal HR and process teams sit at the table where the new rules get written. Applying the same lens to themselves, those teams would invest early in the review process and make sure the organization holds a winning hand rather than reacting once the capability is live.

Pay transparency rules keep expanding across jurisdictions, raising the cost of any pay difference you cannot explain in writing.

External volatility completes the picture. Trade shifts, rate moves, and policy changes push results that executives do not control. Plans need discipline enough to separate leadership impact from market luck, holding leaders accountable without pretending every good quarter was earned.

Two Biases That Distort Executive Pay Strategy

Both errors Polak names have direct parallels in how boards approve packages. Neither one announces itself, and both survive because the resulting decisions look reasonable in the moment.

Proactive bias: pay for agency, not activity

People like to believe they control outcomes, and that belief is useful. Incentive plans should therefore put real weight on results leaders can influence, such as AI adoption tied to measurable business value, pipeline strength, cost discipline, customer retention, and operating resilience.

Agency is not the same as constant motion, though. A plan that pays for launching projects will reliably get projects launched, and it will get them launched whether or not the timing was right. Reward the quality and timing of decisions instead, and say plainly that a well-timed hold counts as a decision.

Question one: Are we rewarding agency or external luck?

Overconfidence: verify impact before paying for conviction

Polak’s second error shows up in talent reviews more often than most teams admit. Charismatic leaders overstate their contribution, while quieter ones frequently create more value than their visibility suggests.

I keep seeing this tension in my work. The leaders who look busiest are often the ones whose decisions are not actually improving the company’s position. My own diagnostic work regularly surfaces where people are chasing the wrong objective, or chasing the right one poorly. The plans rarely catch it in time.

Multiple evidence sources fix most of this. Compare self-assessment against business results, peer input, market position, succession value, and documented outcomes. When the narrative and the evidence diverge, treat the gap itself as data.

Question two: Is this package based on verified impact or on confidence bias?

Two Design Choices: A Game Theory Compensation Strategy Forces

Once the biases are named, two structural choices remain. Each one determines whether a game theory compensation strategy actually changes where dollars land or simply changes how the meeting sounds.

Skill distribution: where pay strategy should stretch

Some roles produce far more leverage than others. Likewise, some people create far more value in the same role. Equal treatment can therefore become strategically unequal.

None of that argues for extreme spreads. It argues for testing whether existing pay differences reflect real differences in impact, scarcity, and replacement risk, and whether the organization can defend each one with evidence rather than tenure or volume.

Replacement risk is the test most plans skip. Ask how long the role would sit open, what a search would cost, and what stalls while the seat is empty. A role that takes nine months to fill and blocks a product line is worth more than its survey median suggests, even when the title reads as ordinary.

Question three: Does our differentiation match the real distribution of value?

Durability: what a resilient pay strategy protects

Short-term heroics hide long-term risk. A leader who hits the number by deferring maintenance, thinning the bench, or burning goodwill has borrowed from a future cycle nobody is measuring yet.

Deferred awards, multi-year measures, and balanced scorecards help here. Together, they let leaders pursue growth while protecting the options the company will need if conditions turn.

Vesting schedules alone will not solve this. The measures themselves have to reach into the years a leader would rather not be judged on, whether that means retention of critical talent, customer renewal rates, or the health of the systems the business runs on.

Question four: Are we rewarding a good-looking fight or a better chance of winning?

Where Familiarity Weakens Incentive Plan Design

The most common calibration failure I see is not political. It is familiarity.

In smaller organizations, leaders know their people directly. They have watched them handle a bad quarter, a difficult client, and a resignation. That closeness feels like superior information, and occasionally it is. More often, it produces a confident narrative built from vivid moments rather than results, and the narrative resists correction because it feels earned.

Familiarity bias does not disappear as headcount grows. Instead, it relocates. In a mid-size company, it lives inside functions, where one leader’s definition of high impact bears little resemblance to another’s. At enterprise scale, it lives in layers, where a director’s reputation travels upward through intermediaries who each smooth the edges.

Three countermeasures work at any size. Require a written reason for every exception, so the narrative has to survive contact with a sentence. Use shared criteria across functions covering business impact, market scarcity, evidence of results, and retention risk. Then add one calibration step in which a peer who does not know the person reviews the recommendation against those criteria.

Under 250 employees, that peer review can be a thirty-minute conversation. Above a few thousand, it needs a defined approval path, a materiality threshold that triggers committee review, and a scenario test asking how the plan behaves if growth stalls or a key market shifts.

Scale changes the machinery, not the principle. A game theory compensation strategy assumes every player holds partial information and a preferred story, including the person running the review.

Spreadsheets deserve a mention here. Version conflicts, manual formulas, and silent overrides let a familiar narrative slip past governance unnoticed, because nothing in the file records who changed what or why.

Thirty-Eight Years of Splitting Jobs Into Smaller Jobs

I spent 38 years in HR. Early in my career, the mandate was still broad: answer the phone in two rings and handle whatever issue was on the other end, whether that meant a benefits question, a grievance, or a hiring decision due that afternoon. Over time, the profession moved in the opposite direction. We created deeper and deeper silos: job families, sub-families, and an ever-expanding universe of benchmarks.

I watched it happen in real time. As a young practitioner, I worked at Abbott and attended a meeting at Pfizer in Manhattan. Much of the industry was in the room. The discussion turned to the Sales Representative role and the case for splitting that single job into specialized variants, including channel, trade, primary, and secondary. I raised my hand and asked whether we were creating new markets rather than reflecting real differences in work. The response was immediate: “Shut up, Abbott.” I walked out with at least eight new job benchmarks that the industry soon treated as essential. What struck me was that the human agency those roles required had not meaningfully changed. Only the audience had.

Every split created a survey line, and every survey line created pressure to pay against it. Within a few cycles, the structure became self-justifying because the data existed, and nobody could argue with data. Looking back, we were not playing the right game. We were differentiating on labels instead of on the capability and judgment the business actually needed, and the labels were easier to defend precisely because they measured so little.

Where a Game Theory Compensation Strategy Beats Job Architecture

I keep meeting with lean consultants on this exact point. In my view, the word “job” is quietly losing relevance. What matters is the role and whether that role is in the contest or sitting on the short list.

Job architecture answers a cataloging question: what work exists, and how should we file it. That question mattered when the filing drove the survey and the survey drove the budget. It answers almost nothing about which roles can change next year’s result, which is the only question a pay plan actually needs settled. Two people can sit in identical job codes while one holds a decision the business cannot afford to lose and the other holds none. No amount of architecture surfaces that difference, because architecture was never built to look for it.

Building real capability that enhances human agency, so people can win, is becoming the central contest. The heavy process and administrative load that delivers no return and no new capability belongs on the removal list. Time and dollars are too scarce to keep carrying work that does not improve the hand the organization holds.

HR has a choice here. It can continue the safe play, doing what it has always known how to do. Alternatively, it can step into the work of helping people feel both safe enough and capable enough to compete, treating employees as players in a live contest rather than resources to be administered. Organizations that make the second choice will set better rules, review the right things, and leave less value on the table.

Looking Ahead: Reward the Win, Not the Optics

The most useful thing I took from that lecture was not about aggression or caution. It was about outcomes.

Leaders should act when action improves the odds. Waiting is correct when waiting creates a better position. Plan design should reinforce the same discipline rather than paying a premium for whichever choice photographs better.

That is why a game theory compensation strategy will matter more in 2027 than it did three years ago. Tight budgets, fast-moving skill values, heavy AI investment, and external volatility all expose weak plan logic quickly. An organization cannot afford to pay for conviction without evidence, or for activity without value.

Market data still belongs in the process. However, benchmarks should inform the decision rather than make it. They tell you what other organizations decided; they cannot tell you where your company intends to win. Every player at the table reads the same surveys. A game theory compensation strategy asks what you know about your own business that no survey can see. For a related MorganHR perspective on tying rewards to business outcomes, see Performance-Based Pay Models: Aligning Goals Effectively.

What could change next quarter: pay transparency requirements continue to expand at the state and international level, and enforcement practice is moving faster than most plan documentation. If your rationale for differentiation lives in someone’s memory rather than in writing, that gap becomes expensive quickly. Boards should also expect AI-driven role revaluation to outrun the annual survey cycle, which may force off-cycle adjustments that the current plan never anticipated. Treat both as design assumptions to revisit before the next planning kickoff, not as surprises to absorb midyear.

Key Takeaways on Game Theory Compensation Strategy

• Reward agency rather than motion, and confirm that leaders can genuinely influence every measure attached to pay.
• Verify impact with multiple evidence sources before approving an unusually large award, and treat familiarity as a bias at every company size.
• Differentiate where evidence shows real gaps in value, scarcity, or results, and document the reasoning.
• Balance upside with durability so a strong year does not quietly weaken the next one.
• Help people feel safe enough and capable enough to compete, rather than simply occupying a job.

Quick Implementation Checklist

1. Identify the three to five outcomes that matter most in 2027.
2. Separate controllable results from external market effects for each one.
3. Define the evidence standard required for high-impact or scarce talent.
4. Review whether current pay differences match actual differences in value.
5. Stress-test the plan under at least two downside scenarios.
6. Confirm who approves exceptions and how each decision gets documented.
7. Review plan behavior after the cycle closes and adjust before the next one.
8. Ask which processes and administrative load no longer earn their keep.

If your 2027 plan still rewards the appearance of action more than the quality of judgment, challenge the design now. Talk with MorganHR about pressure-testing your measures, differentiation, and governance before the dollars get locked in.

FAQ: Game Theory Compensation Strategy in Practice

Game Theory Compensation Strategy for Practitioners

What does this approach add beyond market benchmarking? Benchmarks report what peer organizations paid. However, a game theory compensation strategy asks which outcomes your business should reward, which roles can actually move them, and how much differentiation the evidence supports.

Does this mean paying top performers dramatically more? Not automatically. Instead, the right spread depends on evidence, market scarcity, business impact, and available budget. The goal is defensible differentiation rather than dramatic spreads for their own sake.

Where should a company start if the current plan is too complex? Begin with the four questions in this article, then remove any measure that does not connect to a clear business outcome. Afterward, clarify the evidence needed for differentiated awards and test how the plan behaves under both strong and weak conditions.

Questions From Executives and Board Members

How should we handle results that depend heavily on the economy? Separate controllable and uncontrollable factors wherever possible, for example by pairing a financial result with an operating or execution measure. Thresholds, relative measures, and committee judgment with written rules also help preserve accountability.

What should we review after the 2027 cycle closes? Compare intended behavior with actual behavior. Specifically, look at where managers requested exceptions, which measures created confusion, and where high-impact talent received weak differentiation, then compare payouts against business results.

Documentation and Regulatory Questions

How much of the rationale needs to be written down? Enough that a reviewer outside the conversation could follow the logic a year later. Because pay transparency obligations continue to widen, an undocumented rationale creates exposure well beyond the immediate cycle.

Does this replace legal or pay equity review? No. This framework guides design judgment, while pay equity analysis and regulatory interpretation belong with compensation specialists and legal counsel.