Rock, paper, scissors in macroeconomics

In macroeconomics, rock-paper-scissors aren’t simply a childhood game. It is a metaphor for cyclical dominance among economic strategies, policies, or agents. No single policy remains optimal forever because each one’s success depends on the environment shaped by the policies that came before it. For example, monetary easing (the ‘rock’) may encourage fiscal stimulus (the ‘paper’), which in turn might be countered by the regulatory tightening (the ‘scissors’), eventually bringing the cycle back to monetary easing. This creates a loop rather than reaching a steady equilibrium.

Although the metaphor generally simplifies the complexity of macroeconomic dynamics, it still offers a helpful illustration of how policies can interact in cycles, each one influencing and being influenced by the others. Though it may not fully capture the richness of real-world complexities, it highlights how no policy remains dominant forever, reflecting the fluid and interdependent nature of macroeconomic strategies.

Many economists utilize the RPS model to illustrate how strategic adaptation and counter-adaptation drive ongoing cycles, whether in central bank decisions, energy market planning, or competition among industries.¹ The model highlights three core insights, which are:

1. No strategy is universally dominant: Just as rock can beat scissors but loses to paper, an economic tool may work in one scenario but falter in another.
2. Effectiveness depends on others’ moves: Success hinges on how other players (for example, governments, investors, or firms) respond and adjust.
3. Dynamic cycles, not static outcomes: Instead of settling into a fixed balance, economies often cycle through phases, with each dominant approach eventually giving way to its counter.

Modeling cyclical phenomena

In games like rock-paper-scissors, each choice beats one and loses to another in a continuous loop. This circular, intransitive structure prevents any single strategy from remaining dominant. Similar patterns appear in macroeconomic and social systems, where behaviors cycle rather than settle into stable outcomes.

For example, in public goods settings, shifts in behavior can occur where cooperation decreases as defection becomes more common, followed by a rise in opting out, which may, over time, lead to a resurgence of cooperation. This cycle mirrors the RPS dynamic, illustrating how strategic interactions in the economy can follow predictable, recurring patterns over time.^{2 3}

Strategic interactions and equilibrium

The RPS is a perfect illustration of a mixed-strategy Nash equilibrium, where the optimal approach is to randomize equally among the three choices to remain unpredictable. In practice, maintaining predictability could make one’s strategy more easily anticipated by others. This principle applies to macroeconomic policy settings, where institutions like the Federal Reserve and the Treasury continuously adjust to each other’s moves.

To illustrate, the Federal Reserve may lower interest rates to stimulate growth, prompting the Treasury to adjust fiscal spending in response. Instead of converging on a single, fixed approach, these interactions tend to evolve in a continuous, adaptive cycle. No single policy is universally optimal; therefore, policymakers must stay flexible, adjusting strategies as economic conditions and counterparties evolve.⁴

Anticipating strategic behavior

While the theoretical RPS model assumes perfectly random strategies, real-world behavior often reveals patterns. In repeated games, people and institutions may develop behavioral patterns or respond strongly to recent outcomes, which can sometimes make their future actions easier to anticipate by others.¹ In macroeconomics, this plays out as firms, investors, and policymakers seek to predict and leverage these patterns. For example, investors may shift portfolios ahead of anticipated monetary easing, while businesses accelerate spending in anticipation of fiscal expansion.

The ability to recognize and respond to these patterns can provide a temporary advantage, such as when strategic investors anticipate market movements that may prompt central bank intervention.⁵ However, as other players learn and adapt, those opportunities may fade, reinforcing the principle that adaptability, rather than rigid strategy, is what truly matters.

Captures dynamic competition

The rock-paper-scissors framework captures dynamic competition, where no single player or strategy holds permanent dominance. In industries and markets, this intransitive structure means firms or policies gain an advantage only until their rivals adapt. This concept is central to evolutionary game theory and observed in real-world competitive cycles. This helps analysts appreciate why market leadership often rotates rather than consolidates.⁶

Informing policy design

RPS-inspired game models support more robust policy design by anticipating counter-reactions. In unstable settings like energy markets or monetary-fiscal policy interactions, RPS-style strategic thinking helps policymakers assess how one action (e.g., rate cuts) might trigger a counter-move, such as fiscal tightening. This promotes policies that account for adaptation rather than assuming fixed responses.¹

While the rock-paper-scissors analogy provides a helpful and intuitive way to illustrate strategic cycles and competitive interactions, its direct application in macroeconomics may have certain limitations to consider.

1. Balancing simplicity and complexity in strategic models

The rock-paper-scissors framework provides helpful intuition, but real-world macroeconomic settings usually involve a wider range of policy tools and potential outcomes. Additional factors like expectations, credit constraints, and market frictions can introduce complexities that extend beyond the model’s simplified structure.⁷

2. Assumption of rational, symmetric players

RPS presumes agents select actions randomly and symmetrically. In contrast, macro agents, such as governments, firms, and households, often differ in objectives, access to information, and behavioral biases. Such asymmetries can significantly affect equilibrium dynamics.⁸

3. Considering learning dynamics and bounded rationality

In repeated rock-paper-scissors play, human choices often deviate from pure randomness, showing patterns like ‘win-stay, lose-shift’ or recognizable cycles. Likewise, macroeconomic agents learn, adapt, and adjust their strategies over time. While simple RPS models can offer valuable insights, they may not fully capture these evolving behaviors and cognitive factors that add depth to real-world decision-making.¹

4. Simplified treatment of structural changes and deep parameters

Macroeconomic systems are not static. Over time, structural shifts, such as technological innovation, regulatory changes, and major economic shocks, can fundamentally reshape incentive structures and strategic landscapes. Models like RPS are useful for illustrating dynamic competition within a defined framework. However, when the underlying ‘rules of the game’ evolve, these models may not fully reflect the shifting landscape. Considering deeper, structural changes are important, as strategies and behaviors that appear effective in one setting may need to adapt as conditions change.⁹

As economies grow more complex and interconnected, the rock-paper-scissors framework serves as a useful reminder that no single strategy, policy, or market force remains dominant indefinitely. By appreciating the cyclical nature of economic interactions, both investors and policymakers can develop a more flexible, adaptive approach to decision-making. While the RPS model has its limitations, its core lesson remains relevant: success often depends not on finding a permanent solution but on the ability to anticipate change, adjust strategies, and remain responsive in an evolving landscape.

For more information about how rock-paper-scissors dynamics apply to macroeconomics, you will explore how cyclical dominance, strategic adaptation, and counter-responses shape economic policies, market competition, and evolving decision-making in complex, interconnected systems.

References

¹ Wang Z, Xu B, Zhou H‑J. Social cycling and conditional responses in the Rock‑Paper‑Scissors game. Sci Rep. 2014;4:5830.

² Xu B, Zhou HJ, Wang Z. Cycle frequency in standard Rock–Paper–Scissors games: Evidence from experimental economics. Physica A. 2013 Nov 15;392(22):4997–5005. Available from:
https://www.sciencedirect.com/science/article/pii/S0378437113005578

³ Kleshnina M, Streipert SS, Filar JA, Chatterjee K. Mistakes can stabilise the dynamics of rock‑paper‑scissors games. PLoS Comput Biol. 2021 Apr 12;17(4):e1008523. Available from:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8062094/

⁴ Fornwagner H. Cooperation in dynamic social dilemmas: The role of non-binding pledges and linked games. Games [Internet]. 2021 Mar;12(3):70 [cited 2025 Jun 30]. Available from:https://www.mdpi.com/2073-4336/12/3/70.

⁵ Yang Y, Zhu H. Predictable intervention and information transmission [Internet]. 2019 [cited 2025 Jun 30]. Available from: https://www.mit.edu/~zhuh/YangZhu_PredictableIntervention.pdf.

⁶ TBrockbank E, Vul E. Formalizing Opponent Modeling with the Rock, Paper, Scissors Game [Internet]. Games. 2021 Sep 16;12(3):70 [cited 2025 Jun 30]. Available from: https://www.mdpi.com/2073-4336/12/3/70

⁷ Lucas RE Jr. Econometric policy evaluation: A critique. In: Brunner K, Meltzer AH, editors. The Phillips Curve and Labor Markets. Carnegie-Rochester Conference Series on Public Policy. Vol. 1. New York: American Elsevier; 1976. p. 19–46 [cited 2025 Jun 30]. Available from:
https://people.sabanciuniv.edu/atilgan/FE500_Fall2013/2Nov2013_CevdetAkcay/LucasCritique_1976.pdf

⁸ McAvoy A, Hauert C. Asymmetric evolutionary games [Internet]. arXiv preprint arXiv:1506.07472. 2015 Jun 24 [cited 2025 Jun 30]. Available from: https://arxiv.org/abs/1506.07472

⁹ Fernández-Villaverde J, Rubio-Ramírez JF. How structural are structural parameters? [Internet]. 2007 [cited 2025 Jun 30]. Available from: https://crei.cat/wp-content/uploads/2016/09/rubio.pdf