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ArticlePublished 6 Jul 20262 min readBy Kevin JoginMarkov gamesrepeated gamesfolk theoremmultiagent reinforcement learning

Project ManagementProject Risk ManagementAlgorithms for Decision MakingChapter 24

24Part V · Multiagent Systems

Sequential Problems

Strategic interaction plays out over time, and the other parties adapt as you do. Why long-term relationships cooperate where one-off deals defect.

Chapter 24 of 26 11 min read Original KEVOS® synthesis

Strategic decisions are rarely one-shot. They repeat, they unfold over a project's life, and the other parties learn and adapt just as you do. That single change — a future — transforms what's rational.

Extend the game of Chapter 23 across time and you get a Markov game (a stochastic game): a shared situation that evolves based on the joint actions of all agents, with each agent collecting its own rewards over the sequence. It is the MDP of Part II with more than one decision-maker — and the added twist that everyone is adapting simultaneously.

1The shadow of the future

The most important insight here is why repetition changes everything. In a one-off prisoner's dilemma, defection is rational. But when the interaction repeats — as nearly every real project relationship does — cooperation can become the rational choice, because defecting today invites retaliation tomorrow. This is the shadow of the future: the value of the ongoing relationship disciplines present behaviour. A well-known result confirms that a wide range of cooperative outcomes can be sustained in repeated play, held in place by the credible threat of future response.

Round t cooperate Round t+1 cooperate Round t+2 cooperate the threat of future punishment sustains cooperation today One-off deal → defect  ·  Ongoing relationship → cooperation becomes rational
Figure 1. Across repeated rounds, cooperation holds because betrayal now is punished later. The longer and more certain the future relationship, the stronger its grip on present behaviour — which is why enduring partnerships behave so differently from one-off transactions.

2Learning against a moving target

When agents don't know the game and must learn — multiagent reinforcement learning — a new difficulty appears. Each agent is learning while the others learn too, so from any one agent's view the environment keeps changing: the very thing it's adapting to is itself adapting. This non-stationarity is what makes multiagent learning genuinely hard, and it's why naïvely applying single-agent methods can chase its own tail. Techniques that anticipate others' adaptation, or that converge to equilibrium play, are needed to make progress.

Key idea

A future changes the game. Cooperation that's irrational in a one-shot encounter becomes rational when the relationship continues and defection can be punished. And when everyone is adapting at once, you're optimising against a moving target — the core challenge of learning among others.

What it means in practice

The single most useful lever in multi-party project work is lengthening the shadow of the future: make relationships ongoing, outcomes repeated, and reputations visible, so that cooperating pays and defecting costs. This is precisely why long-term framework partnerships tend to behave better than one-off, lowest-price contracts — the future keeps everyone honest. And when you're negotiating against a party that keeps shifting its approach, don't expect a fixed opponent; anticipate that they're adapting to you, and plan for a moving target.

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