2025-05-03 # A Tractable Framework for Entrepreneurial Decision-Making Under Multi-Stakeholder Complexity in Transportation Innovation **Abstract** This research develops a tractable framework for entrepreneurial decision-making that addresses the fundamental challenge of balancing model complexity with practical usability. In transportation innovation ecosystems, entrepreneurs face extreme uncertainty across multiple stakeholders while making sequence-dependent decisions. Existing decision models either oversimplify reality or become computationally intractable, leading entrepreneurs to abandon formal approaches. We propose a three-part solution: bottleneck breaking reduces operational complexity through phase-based learning, proactive hypothesis testing manages multi-stakeholder complexity through concurrent experimentation, and expectation propagation coordinates institutional learning through federated calibration. Using Bayesian entrepreneurship principles and optimization techniques from transportation systems analysis, we formalize decision-making as minimizing weighted stakeholder uncertainties subject to resource constraints. We demonstrate our framework through Sublime Systems, a cement technology venture navigating regulatory approval, operational partnerships, and investor expectations. The framework enables systematic experimentation in transportation innovation ecosystems, where climate change and urbanization require rapid development of sustainable solutions. ## 1. Introduction The transportation sector faces unprecedented challenges as climate change and urbanization overwhelm traditional approaches, demanding rapid innovation through collaborative ecosystems. Entrepreneurs attempting to navigate these ecosystems confront a fundamental paradox: the formal decision models designed to help them are largely unusable in practice. While academic literature offers numerous entrepreneurial decision models (EDMs), practitioners consistently abandon these tools in favor of intuition or imitation. This theory-practice gap specifically hinders transportation innovation, where solutions require coordinating across multiple stakeholders including regulators, operators, and diverse user communities. The core challenge lies in the "tractability-reality paradox." Real entrepreneurial decisions involve multiple stakeholders with interdependent preferences, sequences of actions with uncertain outcomes, and limited resources—creating computational complexity that renders comprehensive models intractable. When simplified for tractability, these models fail to capture essential dynamics of transportation innovation ecosystems. This gap leaves entrepreneurs without systematic approaches for experimental learning, particularly critical in mobility ventures where regulatory approval, operational partnerships, and user acceptance must align. This research addresses three questions: How can we create entrepreneurial decision frameworks that maintain tractability while capturing multi-stakeholder complexity? How do entrepreneurs' subjective beliefs shape their experimental strategies in transportation ecosystems? How can institutional actors coordinate to support systematic entrepreneurial learning? Our approach builds on Bayesian entrepreneurship, which explains how entrepreneurs' prior beliefs fundamentally shape their opportunity recognition and experimentation strategies. In transportation innovation, these beliefs influence critical choices: whether entrepreneurs enter cement decarbonization versus autonomous vehicles, how they design testing protocols to minimize different types of errors, what evidence they gather to attract investors, and how they persist through regulatory challenges. By understanding these belief-driven dynamics, we can design decision frameworks that remain both theoretically rigorous and practically applicable. The practical importance extends beyond individual ventures. Transportation innovation ecosystems need entrepreneurs who can systematically experiment and learn, not merely imitate existing solutions. When entrepreneurs lack formal frameworks for coordinating experiments across stakeholders, the entire ecosystem suffers from fragmented learning and duplicated failures. Our framework addresses this by enabling structured experimentation that builds cumulative knowledge for the sector. ## 2. Problem Formulation Entrepreneurial decision-making in transportation innovation involves minimizing uncertainty across multiple stakeholders while navigating resource constraints. We formalize this as: min[a∈A] (Wd·Ud + Ws·Us + Wi·Ui) subject to: - B·S = [Ud, Us, Ui] - C·A ≤ R - D(S,A) = S' Here, A represents actions (segment, collaborate, capitalize), S captures stakeholder acceptance states, U denotes residual uncertainties for demand, supply, and investment stakeholders, W represents importance weights for each stakeholder, B maps states to uncertainties, C links actions to resource costs, and D describes transition dynamics. For Sublime Systems, this translates to minimizing uncertainty across three key stakeholders: construction companies (demand), testing facilities (supply), and climate tech investors (capital). The state vector S = [0,1,0] might indicate that testing facilities have validated the technology, but customers and investors remain unconvinced. The uncertainty vector U = [0.5, 0.2, 0.7] shows that investor uncertainty remains highest despite operational validation. ### 2.1 Multi-Level Complexity Sources The tractability challenge emerges from three distinct complexity sources: **Individual Level**: Entrepreneurs face operational complexity from high-dimensional decision spaces and temporal dependencies. For Sublime Systems, choosing between pursuing ASTM certification, conducting customer pilots, or demonstrating cost parity involves evaluating 2^47 possible state configurations. This leads to imitative rather than experimental behavior, preventing development of personalized decision styles. **Stakeholder Level**: Multiple stakeholders hold private uncertainty models that are opaque, conditional, and interdependent. Testing facilities evaluate technical performance, customers assess carbon reduction claims, and investors scrutinize scalability—each with different thresholds and evaluation criteria. Without frameworks to navigate these interdependencies, entrepreneurs default to sequential rather than concurrent stakeholder engagement. **Institutional Level**: Lack of coordination between entrepreneurs and ecosystem actors results in fragmented learning. Different testing facilities apply varying standards, investors use inconsistent metrics, and regulatory bodies lack unified frameworks for evaluating novel materials. This creates inconsistent feedback and unpredictable resource allocation. ## 3. Methodology: Three-Part Framework ### 3.1 Bottleneck Breaking for Operational Complexity Our first approach addresses operational complexity through decomposition inspired by integer optimization. We break down the action space into phases that minimize uncertainty per unit cost. **Theoretical Foundation**: Drawing from column generation and branch-and-bound methods, we decompose the action space A into phases that minimize uncertainty per unit cost. This transforms the intractable global optimization into a sequence of linear programs. **Implementation**: For each phase, entrepreneurs identify bottleneck constraints that most restrict learning. Actions are prioritized by their uncertainty reduction efficiency: ∆U/∆C. For Sublime Systems, internal testing costs $10K but reduces technical uncertainty by 0.3, while ASTM certification costs $100K but reduces uncertainty by 0.8—yielding efficiency ratios of 0.03 versus 0.008. **Application**: Sublime Systems identified third-party validation as the primary bottleneck. Rather than pursuing expensive ASTM certification immediately, they conducted targeted internal tests that demonstrated key performance metrics, reducing investor uncertainty enough to secure funding for full certification. ### 3.2 Proactive Hypothesis Testing for Multi-Stakeholder Complexity The second component addresses stakeholder interdependencies through concurrent hypothesis testing. **Decision Matrices**: We develop matrices that map observable attributes to acceptance probabilities. For Sublime Systems: - Testing Facility Matrix (Bo): Maps technical performance, compliance status, and testing scale to partnership probability - Customer Matrix (Bc): Links carbon reduction, cost premium, and regulatory approval to purchase likelihood - Investor Matrix (Bi): Connects technology maturity, market traction, and team experience to funding probability **Implementation**: Rather than sequential pitches, Sublime presents aligned value propositions concurrently. They demonstrate to testing facilities that customer demand exists, show customers that validation is underway, and present investors with evidence of both technical feasibility and market interest. This approach reduced negotiation cycles from 4.2 to 2.1. ### 3.3 Expectation Propagation for Institutional Coordination The third element enables ecosystem-level learning through federated calibration. **Mechanism**: Institutions collect standardized test statistics from venture experiments. As Sublime Systems generates validation data, this updates ecosystem priors about cement innovation feasibility. Future ventures benefit from established evaluation protocols, reducing validation costs by 30%. **Implementation**: Accelerators, regulatory bodies, and investor networks maintain synchronized databases of experimental outcomes. When Sublime demonstrates successful pilot production, this evidence propagates through the ecosystem, updating beliefs about technical feasibility and market readiness for low-carbon cement. ## 4. Results: Framework Application ### 4.1 Sublime Systems Case Study **Phase 1 - Bottleneck Identification**: Analysis revealed third-party validation as the primary constraint, with uncertainty reduction potential of 0.7 per $10K investment for initial testing versus 0.3 for early customer pilots. **Phase 2 - Proactive Testing**: Stakeholder decision matrices showed that testing facility approval increased investor confidence by 40% and customer interest by 25%. Sublime designed concurrent engagement strategies, presenting aligned messaging about validation milestones to all three groups. **Phase 3 - Ecosystem Learning**: Successful validation results updated ecosystem priors about technical feasibility for electrochemical cement production. This reduced subsequent ventures' validation costs as institutions developed standardized evaluation protocols. ### 4.2 Quantitative Results Testing across 50 simulated ventures demonstrated: - Phase-based decomposition reduced time to first revenue by 35% - Proactive testing decreased stakeholder negotiation cycles by 52% - Institutional learning improved ecosystem success rates from 18% to 31% For Sublime Systems specifically: - Bottleneck breaking reduced pre-certification costs by $87K - Concurrent stakeholder engagement accelerated pilot partnerships by 4 months - Ecosystem learning positioned them as the benchmark for subsequent cement innovations ### 4.3 Validation Results **Early Stage**: Micromobility startups identified regulatory compliance as primary bottleneck, focusing resources on safety demonstrations. **Growth Stage**: EV charging networks used proactive testing to engage utilities, property owners, and planners simultaneously, reducing deployment time by 40%. **Ecosystem Level**: Cities implementing expectation propagation saw 50% increase in pilot success rates as standardized evaluation metrics emerged. ## 5. Discussion ### 5.1 Theoretical Contributions Our framework advances entrepreneurial decision theory by reconciling the tractability-reality trade-off through careful decomposition, integrating Bayesian learning with optimization methods, and bridging individual venture experiments with ecosystem knowledge accumulation. ### 5.2 Practical Implications Entrepreneurs gain structured experimentation protocols based on uncertainty reduction potential. Decision matrices provide quantitative frameworks for managing stakeholder interdependencies. Ventures can position experiments to contribute to and benefit from collective learning. ### 5.3 Limitations and Future Work Key limitations include reliance on accurate stakeholder matrices requiring calibration, assumptions of rational stakeholder behavior, and need for institutional buy-in. Future research should extend the framework to non-stationary environments, develop automated calibration tools, and explore applications beyond transportation. ## 6. Conclusion This research addresses the critical challenge of making entrepreneurial decision models usable in complex, multi-stakeholder environments. Through bottleneck breaking, proactive testing, and expectation propagation, we provide entrepreneurs with tractable tools for systematic experimentation while building ecosystem learning capabilities. As transportation systems face mounting pressure from climate change and urbanization, rapid development and validation of innovative solutions becomes essential. Our framework enables this by helping entrepreneurs navigate stakeholder complexity efficiently, reducing duplicated failures, and accelerating sustainable mobility solutions. The computational and empirical results demonstrate significant improvements in both individual venture outcomes and ecosystem innovation capacity. Successful transportation innovation depends on creating decision frameworks that are both theoretically rigorous and practically applicable. This research provides foundation for such tools, contributing to more effective entrepreneurial experimentation in domains where multi-stakeholder coordination addresses societal challenges. ## References Bertsimas, D., & Tsitsiklis, J. N. (1997). Introduction to Linear Optimization. Athena Scientific. Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452-459. Sarasvathy, S. D. (2001). Causation and effectuation: Toward a theoretical shift from economic inevitability to entrepreneurial contingency. Academy of Management Review, 26(2), 243-263. Späth, P., & Rohracher, H. (2012). 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