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Competitive response modeler

Skill varunk130/claude-code-skills/skills/game-theory/competitive-response-modeler

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npx -y skills add varunk130/claude-code-skills --skill competitive-response-modeler

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Models multi-round competitive interactions: who reacts, how fast, with what move, and what payoff outcome - using reaction functions, commitment value, and signaling theory. Use when planning a pricing change, market entry, product launch, capacity addition, or any move where competitor reaction will determine whether the move pays off.

SKILL.md

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Competitive Response Modeler

The move only matters if you correctly predict the reaction.

What this skill is

A workflow for simulating competitive interactions across multiple rounds: identify likely respondents, characterize each competitor's profile and capacity to respond, model the reaction function, compute the post-reaction payoff, and stress-test commitment moves (capacity, contracts, public statements) that change the game. Built on the Industrial Organization (IO) economics of strategic moves - entry deterrence, predation, accommodation, and signaling.

What it solves

  • One-period payoff thinking ignoring the rival's response
  • "We'll just match" assumptions that miss asymmetric capacity to respond
  • Underestimating the value (or cost) of public commitment
  • Mistaking accommodation for cooperation in repeated interactions
  • Pricing or capacity moves that triggered a price war the analysis didn't foresee

When to invoke

  • Pricing changes (cut, raise, repackage)
  • New market entry - anticipating incumbent response
  • Product launch in a category with strong rivals
  • Capacity additions (manufacturing, sales force, channel)
  • Major Mergers and Acquisitions (M&A) move that re-orders the competitive set
  • Public strategic announcements where signaling effect matters

Phase 1: Identify the competitive set

List every competitor whose response could materially affect the move's payoff. For each:

  • Market share
  • Cost position (low-cost, mid, premium)
  • Strategic focus (where this product or segment sits in their priorities)
  • Recent moves (last 12 months)
  • Public posture (aggressive, accommodating, niche)

Cut the list to 3-5 material respondents. Modeling all isn't useful.

Phase 2: Profile each competitor's response capacity

For each:

  • Means - can they technically and financially respond? (capacity, capability, capital)
  • Motive - do they care about this move? Is it a core market or peripheral?
  • Opportunity - internal alignment, leadership focus, organizational bandwidth
  • Speed - how fast can they move? days, weeks, quarters?
  • History - what did they do the last 3 times something like this happened?

A competitor with means but no motive may not respond. A competitor with motive but no means is loud but harmless. The dangerous response comes from competitors with all three.

Phase 3: Build reaction functions

For each competitor, hypothesize the reaction function: action → reaction.

Examples:

  • If we cut price 10% in segment X → competitor Y likely cuts 8% within 30 days because their Chief Executive Officer (CEO) has publicly committed to share leadership
  • If we add capacity Z → competitor W likely waits and adds capacity only after observing demand absorption
  • If we announce feature F → competitor V probably announces vaporware within 60 days

For each reaction:

  • Probability (low / medium / high)
  • Magnitude
  • Timing
  • Reasoning chain

Phase 4: Multi-round payoff matrix

Build a tree of plausible action-reaction sequences:

Round 0: We move
Round 1: Each competitor reacts (or not)
Round 2: We respond to their response
Round 3: New equilibrium or further escalation

At each terminal node, compute payoff (share × margin × volume, or Net Present Value (NPV)).

The headline question is not "is round 0 profitable?" but "is the terminal equilibrium profitable?" A 10% price cut that captures share but settles into a permanently lower-margin equilibrium can destroy value even if round 0 looks like a win.

Phase 5: Commitment moves

Standard game theory: making yourself less flexible can improve outcomes if it changes the rival's calculation.

Commitment types:

  • Capacity commitment - invest in capacity that only pays back at high volume, signaling commitment to compete hard
  • Long-term contracts - lock customers, raise rival's cost of poaching
  • Public statements - CEO commitment publicly disclosed, harder to walk back
  • Most-Favored-Nation (MFN) clauses - make discounting expensive (price war deterrent or cartel facilitator)
  • Burning bridges - exit an alternative market to focus, signaling commitment

Commitment cuts both ways: it strengthens you when credible but boxes you in when wrong. Score each commitment for credibility (1-5) and reversibility cost.

Phase 6: Signaling

Moves carry information about your type, intentions, and capacity. Consider what each move signals:

  • Tough type - willingness to absorb short-term loss to discipline rivals
  • Accommodating type - willingness to share the market
  • Limited capacity - easier to enter against
  • Unlimited capacity - entry deterrence

Test the signal:

  • What inference would a rational competitor draw from this move?
  • Could you signal the same thing more cheaply with a different move?
  • Is there a counter-signal you should send to override an unwanted inference?

Phase 7: Game-theoretic check - repeated versus one-shot

Most real competitive interactions are repeated. Apply the folk-theorem intuition:

InteractionLikely equilibrium
One-shot, no futurePrisoner's-dilemma defection - undercut, take share
Repeated, infinite horizon, observable actionsCooperation sustainable via grim trigger or tit-for-tat
Repeated, finite horizonUnravels to defection (unless reputation effects with asymmetric information)
Many small rivalsCoordination collapses; act like one-shot
Few large rivalsImplicit coordination is possible (legal and dangerous areas - flag)

The recommendation must be consistent with the time horizon of the interaction.

Phase 8: Recommendation

Synthesize:

  • The move
  • Expected reactions per competitor with probability and magnitude
  • Terminal equilibrium with payoff versus status quo
  • Commitment moves to deploy (or avoid)
  • Signals being sent (intended and inadvertent)
  • Triggers that would cause us to retreat or escalate
  • One unilateral move that improves our position even if no one reacts (insurance)

Output

  • Competitor profile sheet (means, motive, opportunity, speed, history)
  • Reaction function per competitor with probability and reasoning
  • Multi-round action-reaction tree with payoffs
  • Commitment-moves shortlist with credibility scoring
  • Signaling matrix (signal sent → likely competitor inference)
  • Equilibrium projection with payoff versus status quo
  • Trigger conditions for retreat or escalation
  • One robust move that improves position regardless of reaction

Operating rules

Always

  • Identify 3-5 material respondents, not the whole market
  • Score each on means, motive, opportunity, and speed
  • Compute payoff at terminal equilibrium, not just round 0
  • Match the equilibrium concept to the time horizon
  • Test commitment moves for credibility

Never

  • Assume rivals will accommodate
  • Use one-shot logic in a clearly repeated interaction
  • Make commitments you can't sustain
  • Move first without a contingency for retaliation
  • Skip the signal check on any public action

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