Each of the ~1,200 dots in the field is one simulated consumer with their own price sensitivity and brand-loyalty draw. Every quarter each consumer computes a utility score for you and for both rivals and buys from whichever is highest — this is a discrete-choice (multinomial logit) demand model, the standard tool for market-share analysis:
U_firm = Q_firm − β·(price_firm) + γ·log(1 + marketing_firm) + ε (random taste shock)
P(choose firm) = exp(U_firm / T) / Σ exp(U_other / T) (softmax / logit share)
Here Q is product quality (raises utility for everyone), price lowers utility more for price-sensitive shoppers, marketing spend raises awareness which shifts more consumers into the choice set with diminishing returns (log), and ε is an idiosyncratic taste shock unique to each dot — this is why identical offers still split roughly evenly with some noise instead of everyone picking one "best" option.
- Price — higher price raises per-unit margin but linearly lowers your utility score for every price-sensitive consumer, shrinking your share.
- Marketing spend — a quarterly cost that raises awareness (utility) with diminishing returns; useful against a higher-quality rival, wasteful past saturation.
- Quality investment — a one-time-per-quarter R&D style spend that permanently raises your product's baseline utility for all consumers, but costs cumulative profit.
- Run Next Quarter — commits the round: consumers re-decide, rivals adapt their price toward whichever firm is winning share (a simple best-response heuristic), and profit = (price − unit cost) × units − marketing − quality spend.
Real-world relevance: this logit/softmax share model is the same mathematical structure used in real competitive-intelligence and pricing-strategy tools to forecast how a price or marketing move will redistribute market share among competing firms.