More Paths or More Contrast? A Theory of Experimentation Breadth

How should an organisation choose the breadth of its experimentation portfolio? Breadth has two distinct margins—the number of paths kept alive, and the degree of contrast among them—and prior research has largely studied them in isolation. We bring them into a single framework and show that they need not move together. Under a fixed experimentation budget, adding paths creates more chances to find a strong direction, but it also dilutes learning across paths and weakens the strongest feasible contrast. When the task is primarily ranking among already-viable alternatives, broader portfolios become more attractive as the budget rises. When paths share common viability uncertainty, and experimental signals track payoff relatedness, however, additional paths partly repeat the same viability test rather than provide independent information. We identify conditions under which testing exactly two sharply contrasting paths is optimal, dominating both a single deep test and broader portfolios. The framework reconciles competing prescriptions—many parallel shots versus a few sharp comparisons—by clarifying when each applies, and shows why empirical measures of breadth should not treat the number of options and their relatedness as separable margins.