A Prior-Predictive Monte Carlo Framework for Pricing Complex Data Products in Data-Poor Markets

Battle Count: 75

The paper addresses data product pricing in B2B semiconductor contexts, not financial market trading. However, the Monte Carlo simulation methodology, probabilistic price band generation (P5/P50/P95), constraint-truncated priors, and Bayesian updating framework share conceptual similarities with quantitative pricing models used in derivatives valuation and risk management. The log-linear multiplicative model structure is analogous to factor models in finance. The relevance is primarily methodological rather than domain-specific.

The core Monte Carlo simulation is computationally straightforward (O(TN|J|) operations per simulation). The mathematical framework involves standard probability theory, log-normal distributions, and rejection sampling. However, practical implementation requires careful specification of prior distributions, constraint sets, multiplier functions, and the 'mix of deals' distribution - all of which depend on domain expertise. The constraint-truncated prior via rejection sampling is simple but may have low acceptance rates with tight constraints. The online Python implementation is provided.

The paper provides a Monte Carlo engine link (online-python.com/arRMbkCSnB), detailed mathematical formulations, and a complete case study with specific parameter values. However, the model fundamentally depends on expert judgments for prior distributions and multiplier selections, which are inherently subjective and not fully specified. The baseline price anchor derivation is referenced to a prior publication (Zhirnov 2026). The case study is hypothetical rather than based on real transaction data.