1. develop a mathematical formulation for how 🚨 max_theta p(theta|angie), p(theta|charlie)🚨 2. mail to scott given our shared vision for bayesian entrepreneurship and its synergy with my research agenda with charlie on operations for entrepreneurs (i earned his agreement), i'm sharing thoughts synthesized from feedbacks from five people (Angie, Jorge, Kristen, Ararat, Leke) after the session. feedback giver Angie (Jul.18) and feedback synthesizer Angie (Jul.20) have different priors as she calibrated her prior given the three choices angie and charlie made together. given the shared goal, which of the two options is better given our 🚨max_theta p(theta|angie), p(theta|charlie)🚨 ### direct quotes - "optimal choice of entrepreneurial experiments seemed like a quite compelling framework and one that I plan to look more into" - "" ### effective - Mrs.Scott story, especially one cannot define bias in agent's level, nor do one need to - narrative structure of negating the first chunk of your research which gives credential to what you chose - i think revolution from inside is more effective ### rooms for our growth - as i described in my bio attached, essence of bayes is viewing everything as random variable (and the number of variable itself is random and can expand) - focus on bayes rule which is one version of implementation, after we accept the idea - root idea: every variable is random variable - root idea branches to the need for algorithm to explore posterior space one of which is bayes rule, especially when the number of sample is limited, - seeing bigger picture by taking one step back (from bayes rule to every variable is random) will allow discussions as below: - mechanisms to falsify the prior? e.g. prior predictive checks which is generating p(y_tilde) using assumed prior p(theta) and likelihood model p(y|theta), simulation-based calibration, posterior predictive checks - led by andrew gelman et al.'s recognition of the power of simulation in checking the assumptions, and i encourage you to develop definition around "statistical theory will be mostly about gaining a deep understanding of simulation as profitable abstraction of counter-factually repeatable phenomenon." from https://statmodeling.stat.columbia.edu/2020/08/05/somethings-do-not-seem-to-spread-easily-the-role-of-simulation-in-statistical-practice-and-perhaps-theory/. this is relevant to the discussion during bayesian reading group session on experiment. there's type of experiment where founders learn vs not learn. the former is persusion - that being said, three words i'm very unhappy about and would like to sharpen together: - learning: if you create a new thing, is that learning? e.g. - what did i learn from attending your talk? - how scott thinks about subjective, proactive, - how scott's thought has evolved - how econ-based students are thinking about your persuasion (they like modeling, fist half they weren't sure whether 🚨) - what are we learning bayesian entrepreneurship? i feel like we're creating sth and shaping it. hart pozen said everything can be framed as learning and brought passive vs active learning to my attention, but - i really don't can had different opinions on whether experiment can and cannot be the demonstra - - - what are - subjective prior - multi-perspective - game theory ---- identify opportunity ideas are themselves important studying ppl they think they create value (have to learn) see the choices people make cannabis legalization meets internet had Post-Traumatic Stress Disorder ent finance is what happens - operating and finance decision - granual reality of what that statement means should socrates pursue this entrepreneurial opportunity what choices should he 1. 2. how do think about how do you track resource (supply chain, ) don't tell you "what" to do (action oriented) VS don't tell you "how" to do (plan oriented) one's value system earns value when accepted by others so valuable progress analytically residual creativity people want to influence others and environmental approach -> competitive advantage - > connect profit function with competitive advantage environment, strategy, performance env to strategy is commercialziation and disclosure env sell ideas to take best advanage of them market for ideas shapes ; innovation reesatabilith for power no market for ideas > competition with them ; innovation serves schem relative established barrier to entry, innovation use innovation to overturn, established barriers to entry, established sources of competence, and that allows our innovation, career and entrepreneurs to basically, right, Usher, you know, kind of overturn establishment. competition in the product market is it correct to understand 1. sch. argued innovation is used to compete with encumbant, 2. but innnovation also happens via collaobration 3. can't you frame the problem as competing two options - one you compete creative destruction is good for society but not good for you () barriers to entry 🙋‍♀️can we frame the problem as competing two agents - one competing with incumbent and collaborating with incumbent ? acquisitions = failures why isn't the only reason why we see cooperation? disclosure problem arrow's disclosure problem - figuring out the price for an idea requires info which intrisically reduce its value - patent litigation (wind sheild wiper - showed to ford mortor company) verify 1. disclosure impairs the percentage of value you capture (o) 2. the reason why we don't see collaboration is bc increase of value creation is smaller than the decrease of value capture percentage (o) effective ip process of pivots for startup firms (first go to product market, prove work - meaningful threat, financing - pivot to cooperation) hard to both compete and collaborate at the same (both in the product and licencing) 🙋‍♀️pivot from collaborate then compete? (learn) is it that the patents are so important, or that the patents are tied to a regulatory system so that's why we see collaboration? you have written it down, but no property right before getting patent timing of the licenses relative to when they receive patent causal impact of ip rights on the market for ideas resolution of uncertainty ⭐️perspective, kaplan josh, john holtanwanger -mess aghion macro economic consequences, how ru we gonna figure out intellecture move -> take the idea (ent are not animal - shared env where everyone agrees) ip, vc, commercialization costs env. about the env. that haven't been - env. matters a lot but firms themselve don't () - ⭐️ed egan modular tech are bulkt to be inserted big players -> system are built to compete MODULAR vs INTEGRATED ( app doesn't matter (value of idea doesn't matter) -> hustle - steal anyway -> hustle - no one even think of stealing (trust based) vank loan collatorize (home) distribution ideas that can be financed TYOE IF FINANCIAL CONTRACT PLEDGABILITY OF IDEA ⭐️yal harbark -> patent collateral ecomists (westglow) assumes shared prior -> game theory absence of it (cartel can't work without shared prior - > cheat ) different prior information, capabilities, incentive can cause same prior information, capabilities, incentive does it really matter whether it is right or wrong from animal's perspective? as long as humans agree isn't that what's important? entrepreneurship and economics share some element of religion - demand and supply curve. can't imagine the senses you don't have choose a belief (= startup idea) - J bayesian agents each choose one of N actions - an action's probability of success is uncertain. All agents have subjective beliefs about this probability - since agent ⭐️no one is biased from their own perspective ⭐️innovation:=ideas creating value for market place no ground truth / opportunities willing to false positive, but removed false negative allow for false negative but minimizing maximize information value relative to your prior unpersuasive to seqoia, only when they got traction pure bayesian information sense angel to series A (cleaning term sheet- whose buying the experiment ; i don't want these true believer) environmental (recognizing) subjective nature of the environment - once you start grappling with this, more subtle bc not (scott finally agrees with the biased is a wrong framing) -> selection favorable distribution, noisy learning - commitment free learning is limited to noisy 🙋‍♀️can you choose to have different priors for different environments? i use different profile persona for tinder and bumble --- 🟪A12: How the components interact to amplify the anomaly: The decision is not a simple trade-off. The entrepreneur is incentivized to pursue the high-upside V, fundamentally altering the balance between the cost of over-promising (Co) and the cost of under-promising (Cu). 🟩D12: The integration challenge: The central task is to solve this new objective function to find an optimal "promise level" (q*) that balances these competing forces. 🟧G12: The integrated system that emerges: The solution to the model—an explicit formula for the optimal promise level: q* = ln((2Cu + V) / (2Co + V)). This is the core engine of our contribution. 🟥 C12: This unified framework explains that an entrepreneur's apparent "overpromise bias" is not a simple cognitive flaw, but a rational output of their strategic posture. The framework (C12) works by showing how an entrepreneur's strategic lens (C2) determines their perception of the model's parameters (Cu, Co, V), which, when plugged into the prescriptive rule (C1), generates their promise level (q*). It unifies the mindset and the math, reframing bias as a calculated, context-dependent strategy. ### **A Walking Review: Classifying Strategy Literature with the Promise Vendor Lens** This review sorts papers by how they align with our model's logic: - **🟪 A12 (The Anomaly):** Papers that identify a complex puzzle where multiple forces interact, altering a simple trade-off. - **🟧 G12 (The Engine):** Papers whose core contribution is a formal model or mechanism that provides a direct solution. - **🟥 C12 (The New Lens):** Papers that offer a unified framework or paradigm, reframing a known phenomenon and changing how we think about strategy. --- ### 🟪 A12: Papers that Define the Anomaly _(These papers highlight a complex interaction that breaks a simple trade-off, just as `V` alters the `Co` vs. `Cu` balance.)_ - **Gans, Stern & Wu (2019) – “Foundations of entrepreneurial strategy”:** Identifies the paradox of "strategic indeterminacy," where entrepreneurs face multiple equally good paths. The anomaly is that standard optimization fails, and the real challenge becomes the _process of choosing_ itself. - **Gans & Stern (2017) – “Endogenous Appropriability”:** Pinpoints the anomaly that the ability to profit isn't a given; it's a choice. Entrepreneurs must actively build "endogenous appropriability," fundamentally altering the simple view of them as passive players in a fixed environment. - **Lamoreaux & Sokoloff (2001) – “Market Trade in Patents and the Rise of a Class of Specialized Inventors in the 19th Century United States”:** Uncovers the historical anomaly of a thriving 19th-century "market for ideas." The existence of specialized inventors who only sold patents complicates the simple narrative of the lone inventor-entrepreneur. ### 🟧 G12: Papers that Provide the Engine _(These papers deliver a formal model or explicit mechanism that solves a complex trade-off, like our `q*` formula.)_ - **Anton & Yao (1994) – “Expropriation and Inventions: Appropriable Rents in the Absence of Property Rights”:** Provides the game-theoretic "engine" for selling an idea without IP. The model of partial, strategic disclosure is the explicit mechanism that solves the information paradox. - **Gans, Hsu & Stern (2008) – “The Impact of Uncertain Intellectual Property Rights on the Market for Ideas”:** Delivers a dynamic game model that acts as the engine for decision-making under IP uncertainty. The model explicitly shows how uncertainty levels shift the optimal strategy from cooperation to competition. - **Luo (2014) – “When to Sell Your Idea: Theory and Evidence from the Movie Industry”:** Offers an "optimal stopping" model as the engine to solve the "when to sell" problem. It provides a formal rule for balancing the value of further development against market risk. - **Malmendier & Lerner (2010) – “Contractibility and the Design of Research Agreements”:** Builds the contract theory "engine" for R&D deals with incomplete information. The model specifies how to use mechanisms like milestones and options to align incentives. - **Gambardella, Gius & Stern (forthcoming) – “Homo Entrepreneuricus”:** Proposes a "Behavioral-Bayesian" model as the engine to understand entrepreneurial decisions. It formally integrates psychological biases with rational updating. ### 🟥 C12: Papers that Offer a New Lens _(These papers provide a unified framework that reframes a behavior or choice as rational and context-dependent, just as we reframe "overpromise bias.")_ - **Teece (1986) – “Profiting from Technological Innovation: Implications for Integration, Collaboration, Licensing and Public Policy”:** The foundational "Profiting from Innovation" framework. It reframes the puzzle of why innovators fail to profit by showing that value capture is a rational, predictable outcome of the interplay between IP and complementary assets. - **Gans, Hsu & Stern (2002) – “When Does Start-up Innovation Spur the Gale of Creative Destruction?”:** Offers the "deal or duel" framework. It reframes the startup-incumbent interaction not as a default war, but as a rational choice contingent on IP strength. - **Arora, Fosfuri & Gambardella (2001) – “Markets for Technology: The Economics of Innovation and Corporate Strategy”:** Establishes the "markets for technology" paradigm. It reframes corporate strategy by showing that specializing in innovation and trading ideas is often a more rational and efficient system than vertical integration. - **Hellmann & Puri (2000) – “The Interaction between Product Market and Financing Strategy: The Role of Venture Capital”:** Provides a unified theory of "finance-strategy fit." It reframes financing not as a separate decision, but as a strategic tool that must be rationally aligned with the speed and nature of the product market. - **Eesley, Hsu & Roberts (2014) – “The Contingent Effects of Top Management Teams on Venture Performance: Aligning Founding Team Composition with Innovation Strategy and Commercialization Environment”:** Delivers the "team-strategy fit" prescription. It reframes the concept of a "good team" from a list of universal traits to a context-dependent alignment between team skills and strategic needs. - **Hsu (2006) – “Venture Capitalists and Cooperative Start-up Commercialization Strategy”:** Presents a unified view of "VCs as strategic catalysts." It reframes the value of venture capital beyond money, showing it provides a rational pathway to a cooperative exit by signaling quality and reducing information friction. --- 2025-07-06 using the labels from [[🐢Bayesian Operations for Entrepreneurs]], and [gpt process](https://chatgpt.com/c/686ac2a3-f360-8002-8cc5-29c5558bdd6e) | Code | Canonical meaning (your framework) | Concrete label(s) I used in this batch | | ---------- | ------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **🟪 A1** | _Different Maps_ – belief / information heterogeneity | “Heterogeneous Priors”; “Information Paradox”; “Entrepreneurial Biases”; “Patent Value Uncertainty”; “Hidden Startup Types” | | **🟪 A2** | _Different Clocks_ – tempo / timing heterogeneity | “Premature Pivoting”; “Timing Trade‑off”; “Adaptive vs Intuitive” (experimentation tempo); “Strategy‑Shift Clockspeed” | | **🟪 A12** | _Bridge of Heterogeneity_ – priors **and** tempos collide | “Conflated Signals”; “Institutional Gap”; “Specialized Innovator Emergence”; “IP Uncertainty Friction”; “Science‑Market Disconnect”; “Preemptive Deals” | | **🟩 D1** | _Need to Predict Commitments | “Cognitive Calibration”; “Data‑Driven Decision Need” | | **🟩 D2** | _Need to Synchronize Clockspeed_ | “Need for Update (sequential)”; “Timing Strategy”; “Staged Value Realization”; “Sequential Strategy Adaptation” | | **🟩 D12** | _Need to Harmonize Commitments & Speeds_ | “Guided Experimentation”; “Open Innovation Need”; “Team‑Strategy Fit”; “Deal‑or‑Duel Choice”; “Bridge Mechanism (patents)”; “Financing‑Strategy Alignment”; “Innovative Contracts” | | **🟧 G1** | _Quality‑Driven Commitment Engine_ – analytic model of priors | “Game‑Theoretic Model”; “Signaling Model”; “Strategic Bargaining Model”; “Behavioral‑Bayesian Model”; “Contract‑Theory Model” | | **🟧 G2** | _Clockspeed Synchronization Model_ – analytic model of timing | “Bayesian Decision Model”; “Dynamic Game Model with IP Timing”; “Optimal Stopping Model (idea sale)”; “Dynamic Strategy Model” | | **🟧 G12** | _Integrated Response Framework_ – combines priors & clocks | “Bayesian Trial Framework”; “Integrated Choice Model”; “Holistic Synthesis”; “Empirical Classification (startup strategy index)”; “Integrative Framework of Complementary Assets” | | **🟥 C1** | _Effectiveness Prescription_ – quality/accuracy pay‑off | “Improved Venture Outcomes”; “Patents’ Payoff Quantified”; “Validated Learning Yields Success”; “Timing Strategy Guidance (when‑to‑sell)” | | **🟥 C2** | _Efficiency Prescription_ – speed/cost pay‑off | “Appropriability without IP”; “Efficient Collaboration Design”; “VC as Strategy Catalyst” | | **🟥 C12** | _Unified Coordination Toolkit_ – joint quality–tempo insight | “Strategic Choice Design”; “Paradigm of Open Innovation”; “Commercialization Strategy Compass”; “Theory of Innovation Value Capture”; “Deal‑oriented Strategy Lens”; “Policy & Strategy Implications” | | **Paper (Authors, Year)** | **🟪 Anomaly** | **🟩 Develops** | **🟧 Grows** | **🟥 Contribution** | | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Agrawal, Gans & Stern (2021)** – _Enabling entrepreneurial choice_ (Management Science) | A12: **Conflated Signals** – One experiment confuses idea quality with strategy efficacy, creating a dual-uncertainty paradox (idea vs. execution). _Multi-dimensional uncertainty_ | D12: **Guided Experimentation** – Need to lower testing costs and sequence multiple strategy trials based on priors, often via mentor judgment, to disentangle signals. _Planned pivot sequencing_ | G12: **Bayesian Trial Framework** – Proposes an experimental approach with prior-updating (multiple strategy tests) instead of a one-shot bet, integrating learning into venture strategy. _Sequential hypothesis testing_ | C12: **Strategic Choice Design** – Prescribes institutions (accelerators, mentors) that aggregate judgment to help founders choose optimally, bridging effective discovery with efficient decision-making. _Optimizing entrepreneurial trials_ | | **Agrawal et al. (forthcoming)** – _Foundations of Bayesian Entrepreneurship_ (in _Bayesian Entrepreneurship_) | A1: **Heterogeneous Priors** – Entrepreneurs start with diverse beliefs about opportunities (“different maps”), leading to varying interpretations of the same information. _Heterogeneous priors_ | D2: **Need for Update** – Calls for systematic belief-updating mechanisms; entrepreneurs must learn then act in stages, avoiding premature conclusions. _Sequential learning need_ | G2: **Bayesian Decision Model** – Introduces a formal model of venture decisions as Bayesian updates (posterior beliefs drive strategy), capturing experimentation over time. _Belief-updating model_ | C12: **Entrepreneurial Learning Paradigm** – Frames entrepreneurship as a process of iterative learning and choice under uncertainty, bridging strategy and Bayesian inference to guide theory and practice. _Bayesian strategy framework_ | | **Arora, Fosfuri & Gambardella (2001)** – _Markets for technology and their implications for corporate strategy_ (Industrial & Corporate Change) | A12: **Institutional Gap** – Challenges the assumption that innovators must integrate; demonstrates the emergence of a “market for ideas” where specialized innovators sell technology to incumbents. _Market for ideas paradox_ | D12: **Make-or-Sell Dilemma** – Firms need a strategy to either develop innovations in-house or license/acquire externally, harmonizing internal R&D with external opportunities. _Internal vs. external commercialization_ | G0: **Conceptual Framework** – Develops an economic framework (no new math model) defining conditions for tech trading (IP strength, complementary assets) and how those shape corporate strategy. _Tech trade conditions_ | C12: **Strategic Integration** – Broad theoretical integration showing that active technology markets allow efficient specialization: innovators focus on inventing while incumbents focus on scaling, altering classic profiting-from-innovation models. _Specialization via tech trade_ | | **Camuffo, Cordova, Gambardella & Spina (2020)** – _A scientific approach to entrepreneurial decision making: Evidence from a RCT_ (Management Science) | A2: **Premature Pivoting** – Entrepreneurs often pivot too quickly or not at all, a tempo misfit where they either rush changes or hesitate, leading to suboptimal learning. _Impatient iteration_ | D12: **Evidence-Based Navigation** – Need to harmonize learning and action: entrepreneurs should systematically test assumptions (like scientists) before major pivots, aligning decision speed with evidence. _Structured decision discipline_ | G12: **Lean Experimentation Method** – Implements a “scientific” decision method (hypothesis-driven entrepreneurship) in a field experiment. This process model integrates data collection and analysis into venture development. _Hypothesis-driven startup process_ | C1: **Improved Venture Outcomes** – Shows that adopting scientific decision practices (structured experiments) yields more persistent yet validated opportunities (founders avoid false negatives), thereby increasing venture success. _Scientific method payoff_ | | **Gans, Hsu & Stern (2002)** – _When does start-up innovation spur the gale of creative destruction?_ (RAND Journal of Economics) | A1: **Cooperate vs. Disrupt** – Not all innovations overthrow incumbents; anomaly in which some start-ups partner with incumbents instead of destroying them, defying the pure “creative destruction” expectation. _Dual commercialization paths_ | D0: **Strategy Choice** – Entrepreneurs must navigate a core strategic choice: fight incumbents head-on or collaborate via licensing/acquisition. Identifying which path maximizes value is the key need. _Entry vs. alliance decision_ | G1: **Game-Theoretic Model** – Provides a formal model of start-up–incumbent interaction under different conditions (e.g., IP strength). A symmetric information model yields thresholds when deals occur versus competition. _Deal-or-entry model_ | C12: **Conditional Strategy Framework** – Prescribes that strong intellectual property and high transaction efficiency favor cooperation (market for ideas), whereas weak IP forces market competition. This bridged understanding guides entrepreneurs on when to sell or go it alone. _“Deal vs. duel” guidelines_ | | **Gans, Stern & Wu (2019)** – _Foundations of entrepreneurial strategy_ (Strategic Management Journal) | A0: **Degenerate Options** – Entrepreneurs often face multiple equally viable strategies with no clear single optimum, an endogenous paradox where standard optimization fails. _Strategic indeterminacy_ | D0: **Choice Constraint** – Need to choose one path despite resource limits. Entrepreneurs must deliberately **forgo** attractive alternatives when they cannot pursue all, requiring a method to navigate surplus options. _Forced strategic trade-off_ | G12: **Integrated Choice Model** – Develops an integrated framework linking uncertainty and learning: as founders gather noisy information, they reach a “flat maximum” scenario of strategy choices. The model emphasizes process (choice architecture) over static solution. _Uncertainty–learning interplay model_ | C0: **Reframed Strategy Logic** – Reconceptualizes entrepreneurial strategy by highlighting _choice_ itself as central, not just analysis of environment. The contribution is a paradigm treating multiple viable strategies (degeneracy) as a feature of entrepreneurship, requiring proactive elimination of options. _Choice-centric strategy_ | | **Anton & Yao (1994)** – _Expropriation and inventions: appropriable rents in the absence of property rights_ (AER) | A1: **Information Paradox** – How can inventors profit when ideas can be stolen? In a no-patent world, asymmetric information (inventor vs. buyer) creates belief gaps about invention value. _IP absence dilemma_ | D12: **Reveal vs. Conceal** – Need to balance disclosing enough to attract buy-in while preventing idea theft. The inventor must harmonize trust and secrecy in negotiations. _Selective disclosure challenge_ | G1: **Signaling Model** – Offers a game-theoretic model where inventors partially reveal quality signals to strike deals without formal IP. Equilibrium shows some rent can be secured via clever information release. _Partial disclosure mechanism_ | C2: **Appropriability without IP** – Shows inventors can capture some value even absent legal protection, though not fully efficiently. Implies that improving contract institutions (or IP rights) would increase innovation efficiency. _Innovation rent with weak IP_ | | **Arora (1995)** – _Licensing tacit knowledge: Intellectual property rights and the market for know-how_ (EINT) | A12: **Tacitness Barrier** – Tacit know-how is hard to codify or protect, posing an anomaly: how to trade knowledge that isn’t fully patentable. _Uncodified knowledge gap_ | D12: **Knowledge Transfer Design** – Need contractual and organizational solutions (training, alliances) to transfer tacit expertise without losing value. Aligning incentives for teacher (licensor) and learner (licensee) is critical. _Tacit transfer arrangements_ | G2: **Contracting Model** – Develops a model for licensing agreements including know-how transfer (e.g. contingent contracts, royalties, training periods). Often a dynamic approach (learning by doing over time) is modeled to address gradual knowledge diffusion. _Tacit licensing model_ | C12: **Extended Appropriability Theory** – Expands understanding of markets for technology by incorporating tacit knowledge. Suggests that even “unwritten” know-how can be sold via relationship-based contracts, bridging formal IP theory with real-world practice. _Selling the intangible_ | | **Arora, Fosfuri & Gambardella (2001)** – _Markets for Technology: The Economics of Innovation and Corporate Strategy_ (MIT Press) | A12: **Specialized Innovator Emergence** – Observes a broad phenomenon: specialized technology suppliers exist, defying the vertically integrated R&D model. This heterogeneity across industries signals a systemic shift. _Specialist vs. integrator_ | D12: **Innovation Strategy Alignment** – Firms must decide whether to focus on innovation and sell IP or to integrate and commercialize themselves. Strategy must align with industry conditions (IP regime, complementary assets availability). _Make–buy innovation strategy_ | G12: **Holistic Synthesis** – Provides an integrative framework (case studies, theory) covering multiple models and examples of technology transactions. It bridges microeconomic models with strategy, offering a taxonomy of market-for-ideas mechanisms across contexts. _Unified tech-market framework_ | C12: **Paradigm of Open Innovation** – Delivers a comprehensive theoretical foundation showing that trading technology can enhance the innovation ecosystem’s efficiency. It influences policy (support IP markets) and corporate strategy (partner vs. build), effectively bridging innovation economics and strategy. _Markets-for-technology paradigm_ | | **Chatterji & Fabrizio (2016)** – _Does the market for ideas influence the rate and direction of innovation?_ (SMJ) | A12: **Collaboration Shock** – A legal shock hampered incumbent–inventor collaborations, revealing an anomaly: when idea markets break down, incumbents’ innovation rate and quality drop dramatically. _Broken pipeline effect_ | D12: **Open Innovation Need** – Firms need mechanisms to source external ideas (e.g. partnerships with inventors). Without this, they struggle to innovate in complex domains (like medical devices), highlighting the need to integrate external knowledge. _External sourcing imperative_ | G0: **Natural Experiment** – Uses an exogenous policy change as an empirical model to examine innovation outcomes. The analysis (difference-in-differences) tracks how blocked collaborations affect patents and quality, rather than introducing a new theoretical model. _Policy shock analysis_ | C12: **Evidence for Idea Markets** – Provides causal evidence that open idea exchange increases innovative output and quality (firms without access shifted to less novel, lower-quality patents). Supports policies and strategies fostering collaboration for both effective (high-quality) and efficient (faster) innovation. _Value of open innovation_ | | **Eesley, Hsu & Roberts (2014)** – _Contingent effects of top management teams on venture performance: Aligning team composition with strategy and environment_ (SMJ) | A12: **Misalignment Risk** – The impact of founding team makeup on performance is context-dependent. A “great team” on paper can underperform if their skills don’t fit the innovation strategy or market environment (strategy–team mismatch). _Contextual team fit_ | D12: **Team–Strategy Fit** – Need to align team composition with venture strategy and commercialization context. Entrepreneurs must recruit or structure leadership to complement their innovation type (e.g. R&D-intensive vs. market-driven) and external environment. _Adaptive team building_ | G12: **Contingency Analysis** – Employs an empirical contingency model: interactions between team characteristics and external factors predict performance. No single optimal team—uses statistical models to show different combinations win under different conditions. _Fit-dependent performance model_ | C1: **Alignment Prescription** – Recommends matching cofounders’ expertise to the venture’s strategic focus and industry setting for superior outcomes. Demonstrates that **effectiveness** in startups comes from the right team–strategy alignment, not one-size-fits-all team quality. _“It depends” principle_ | | **Farre-Mensa, Hegde & Ljungqvist (2020)** – _What is a patent worth? Evidence from the U.S. patent “lottery”_ (Journal of Finance) | A1: **Patent Value Uncertainty** – Innovators and investors held conflicting beliefs on how much a patent truly contributes to a startup’s success. The value of patents was an open question with anecdotal heterogeneity. _Patent value debate_ | D0: **Appropriation Decision** – Need to decide whether seeking patents is worth the cost. Entrepreneurs require hard evidence on payoff (funding, growth) to guide IP strategy under uncertainty. _IP strategy choice_ | G0: **Quasi-Experiment** – Leverages a “lottery” of patent examiners as a natural experiment to isolate patent effects. By comparing startups that barely got a patent vs. barely didn’t, it quantifies causal impact without a new theoretical model. _Patent lottery analysis_ | C1: **Patents’ Payoff** – Finds patents significantly boost firm outcomes (e.g. financing, scaling), providing concrete evidence that securing IP can be an **effective** strategy for startups. This informs entrepreneurs and policymakers of the tangible private value of patenting. _Patent premium quantified_ | | **Gambardella, Gius & Stern (forthcoming)** – _Homo Entrepreneuricus_ (in _Bayesian Entrepreneurship_) | A1: **Entrepreneurial Biases** – Identifies that entrepreneurs deviate from Homo economicus: they have unique prior beliefs, overconfidence, and heuristics that standard models don’t capture (different mental maps). _Entrepreneurial cognition_ | D1: **Cognitive Calibration** – Entrepreneurs need frameworks to check and update their biased priors (avoid “learn-only” analysis paralysis or “act-only” overconfidence). The paper emphasizes incorporating cognitive science to calibrate decision-making. _Debiasing and updating need_ | G12: **Behavioral-Bayesian Model** – Proposes a model blending behavioral factors with Bayesian updating – entrepreneurs as “Homo Entrepreneuricus” who update beliefs but with subjective priors and biases. This hybrid model addresses both rational learning and psychological factors. _Bayesian-behavioral fusion_ | C12: **Behavioral Entrepreneurship Theory** – Contributes a richer theory that bridges psychology and strategy: entrepreneurs can be modeled as Bayesian decision-makers with personal twists. Suggests training and policies to account for these human factors, integrating prescriptive insights on how founders should adapt their decision processes. _Bayesian entrepreneurial mindset_ | | **Gans (2017)** – _Negotiating for the Market_ (Advances in Strategic Management, Vol. 37) | A12: **Preemptive Deals** – Highlights an anomaly in tech markets: rather than competing in a new market (“in the market”), startups and incumbents often negotiate **for** the market (one acquires control). This defies the assumption of inevitable market competition by showing an alternate path via deal-making. _End-run competition_ | D12: **Deal-or-Duel Choice** – Entrepreneurs need to decide whether to enter a market or sell/partner beforehand. This requires balancing the value of independent play vs. collaborating with a powerful incumbent – a navigation of conflicting incentives between parties. _Market entry vs. exit strategy_ | G1: **Strategic Bargaining Model** – Introduces a strategic model analyzing bargaining outcomes when a startup can negotiate transfer of a market (e.g. acquisition) instead of head-to-head competition. It identifies conditions (bargaining power, outside options) under which a deal occurs. _For-the-market game_ | C12: **Policy and Strategy Implications** – Shows that the prospect of negotiation changes innovation incentives (e.g. startups may innovate _to be acquired_). Bridges industrial organization and strategy by explaining when markets will be served via hierarchy (merger) vs. competition, guiding antitrust and startup strategies alike. _Deal-oriented strategy lens_ | | **Gans, Hsu & Stern (2008)** – _The impact of uncertain intellectual property rights on the market for ideas_ (Management Science) | A12: **IP Uncertainty Friction** – When patent protection is uncertain (e.g. pending or weak enforcement), the market for ideas falters. Startups and incumbents face a misfit: they can’t trust deals fully, altering the usual cooperate-vs-compete calculus. _Patent uncertainty hurdle_ | D2: **Timing Strategy** – Need to decide whether to wait for IP to solidify or proceed without it. Entrepreneurs must manage a sequential dilemma: delay partnering until rights are clear (slowing progress) versus moving ahead and risking expropriation. _Wait-or-go decision_ | G2: **Dynamic Game Model** – Extends the collaboration vs. competition model by adding a stage of patent uncertainty. A two-stage game (pre- and post-IP resolution) shows that higher uncertainty pushes startups toward building a venture (competition) since secure deals are harder. _Two-stage IP game_ | C2: **Importance of IP Clarity** – Concludes that strengthening IP certainty can make idea markets more **efficient** (more licensing, less duplicative competition). It provides theoretical support for policies improving patent clarity and for startups to strategically patent to enable cooperation. _Secure IP, smoother deals_ | | **Gans & Stern (2000)** – _Incumbency and R&D incentives: Licensing the gale of creative destruction_ (JEMS) | A1: **Creative Destruction Detour** – Presents the puzzle of incumbents sometimes encouraging new innovation through licensing rather than being displaced by it. Incumbents can remain atop the “gale” by striking deals, which contrasts with the expected incumbent–entrant war. _Incumbent–entrant paradox_ | D12: **Co-optive Strategy** – Need for incumbents and startups to align incentives. Rather than racing, they can design licensing or acquisition arrangements so both benefit (incumbent preserves position; startup gains reward). _Collaborative innovation incentive_ | G1: **Innovation Race Model** – Offers a formal model of R&D competition where an entrant innovates and an incumbent decides whether to license or fight. The model shows how incumbency can increase R&D incentives if licensing is feasible, by internalizing the threat. _License-or-fight model_ | C12: **Bridging Schumpeter and Coase** – Integrates the Schumpeterian idea of destruction with Coasian bargaining: demonstrates conditions where the market for ideas allows incumbents to maintain innovation leadership through cooperation. This theoretical insight bridged innovation economics with strategy by showing incumbents can pay for peace. _Cooperative Schumpeterianism_ | | **Gans & Stern (2003)** – _The product market and the market for ideas: Commercialization strategies for technology entrepreneurs_ (Research Policy) | A1: **Dual Market Paths** – Observes that technology entrepreneurs face two markets — product and idea — as alternative paths to commercialization, an anomaly relative to the one-path (product) assumption. _Two-route opportunity_ | D0: **Strategy Selection** – Entrepreneurs need to determine their commercialization route: either build a product/service company or sell/license the innovation. This entails evaluating internal capabilities vs. partnering opportunities from the start. _Route-to-market choice_ | G0: **Framework & Typology** – Provides a conceptual framework and case-based evidence categorizing startup strategies (integration vs. licensing). Rather than a novel model, it lays out a typology of factors (IP, competition, asset needs) influencing strategy choice. _Strategy typology_ | C12: **Entrepreneurial Strategy Playbook** – Contributes a practical and theoretical guide for entrepreneurs: emphasizes assessing both product-market and idea-market options. Bridges theory with actionable insight by linking market conditions to recommended startup strategies (e.g. license when incumbents hold necessary assets, go alone when you can build them). _Commercialization strategy compass_ | | **Guzman & Li (forthcoming)** – _Measuring founding strategy_ (Management Science) | A1: **Latent Strategy Heterogeneity** – Not all startups are alike at founding; there are unobserved strategic orientations (e.g. high-growth “unicorn” vs. niche small business) that are not captured by traditional metrics, indicating hidden heterogeneity in entrepreneurial approaches. _Hidden startup types_ | D0: **Need for Strategic Metrics** – To manage and research startups effectively, we need reliable measures of founding strategy. This paper identifies the operational need to quantify a startup’s strategic intent early (for investors, policymakers, founders themselves). _Early-stage strategy signals_ | G12: **Empirical Classification** – Develops a measurement model using data (registrations, founding team, IP, etc.) to categorize startups’ strategies. Likely uses machine learning or econometric clustering, bridging qualitative strategic typologies with quantitative indicators. _Startup strategy index_ | C12: **Strategy–Outcome Link** – Offers a new tool linking founding strategy to eventual outcomes, enabling broader theoretical and practical implications. By measuring strategy, it bridges entrepreneurial theory with empirical analysis, and guides decision-makers (e.g. targeting support or capital to particular strategy types). _Founding strategy revealed_ | | **Koning, Hasan & Chatterji (2022)** – _Experimentation and start-up performance: Evidence from A/B testing_ (Management Science) | A1: **Adaptive vs. Intuitive** – Some startups systematically experiment (A/B test) while others rely on intuition. This behavioral heterogeneity is an anomaly given the expectation that all data-driven approaches would be uniformly adopted in high uncertainty environments. _Experimentation divide_ | D1: **Data-Driven Decision Need** – Entrepreneurs need to resolve the “one-step split” between acting on gut instinct versus incorporating structured experiments. The paper underscores the need for startups to adopt an experimental mindset to avoid stagnation from guesswork. _Embed experimentation in decisions_ | G0: **Field Evidence** – Uses statistical analysis of startups that implement A/B testing versus those that don’t. Rather than introducing a new model, it leverages observational data (and perhaps instrumental variables) to assess the impact of experimental methods on growth. _A/B adoption study_ | C1: **Validated Learning Yields Success** – Finds that startups engaging in disciplined experimentation perform better (e.g. higher conversion, growth rates), implying that embracing **effective** scientific testing improves outcomes. This provides prescriptive insight that iterative testing is not just hype but adds real value. _A/B testing payoff_ | | **Marx, Gans & Hsu (2014)** – _Dynamic commercialization strategies for disruptive technologies: Evidence from the speech recognition industry_ (Management Science) | A2: **Timing of Strategy Shift** – Startups with disruptive tech often change strategies over time (e.g. from licensing to product or vice versa). The anomaly is that the “best” commercialization mode is not fixed – it evolves as technology and markets mature (a clockspeed issue). _Evolving strategy puzzle_ | D2: **Sequential Strategy Adaptation** – Entrepreneurs need to manage a two-step (or multi-step) process: an early strategy that might differ from a later one. They must learn and pivot their commercialization approach as conditions change, rather than stick to an initial plan. _Pivot timing management_ | G2: **Longitudinal Case Analysis** – Combines industry case history with a dynamic perspective, possibly using a multi-period model or simulations. It traces how strategy choices (OEM licensing, direct entry, etc.) unfold over a technology’s life cycle, solving for optimal switch points. _Dynamic strategy model_ | C12: **Contingent Path Theory** – Demonstrates that the path to market for disruptive tech can involve planned transitions (not a single strategy). This contributes a nuanced theoretical lens: successful commercialization may require **efficient** early moves (alliances when young) followed by **effective** later moves (going direct when strong). It bridges static strategy theories with a temporal dimension. _Two-stage commercialization insight_ | | **Gans & Stern (2010)** – _Is there a market for ideas?_ (Industrial & Corporate Change) | A1: **Cross-Industry Puzzle** – Asks why markets for ideas thrive in some sectors (biotech) but not in others. Variation in idea trading activity is the anomaly investigated, challenging any uniform view of innovation markets. _Uneven idea markets_ | D0: **Environmental Scan** – Entrepreneurs and policymakers need to assess the “market for ideas” in their domain. The paper highlights the need to identify whether conditions (IP regime, tacitness, buyer-seller matching) support an active idea marketplace or not. _Assessing idea market viability_ | G0: **Empirical Survey** – Provides empirical patterns and qualitative analysis rather than a new formal model. It surveys instances of idea transactions and their drivers, effectively creating a diagnostic framework for idea market presence (e.g. metrics of licensing prevalence). _Market prevalence analysis_ | C12: **Boundary Conditions** – Concludes that markets for ideas are not ubiquitous; they depend on factors like appropriability and complementary assets. This nuanced contribution integrates theory with real-world observation, guiding where an open innovation approach is **effective** and where internal development remains necessary (thus informing both strategy and policy). _When ideas trade (and when not)_ | | **Gans & Stern (2017)** – _Endogenous appropriability_ (AER P&P) | A0: **Assumption Inversion** – Traditional models treat appropriability (ability to capture returns) as exogenous; this work identifies it as partly endogenous – entrepreneurs can shape how much of the value they keep (e.g. via strategy or technology choices). This overturns a core assumption, revealing a hidden anomaly in standard theory. _Appropriability as choice_ | D0: **Designing for Appropriability** – Need for entrepreneurs to proactively design strategies (patenting, secrecy, complementary assets) that increase their share of value. Instead of taking the environment as given, they must navigate choices that affect appropriation. _Appropriability strategy need_ | G1: **Model Extension** – Presents a stylized model where the innovator’s actions (e.g. development of complementary assets, contract design) feed back into the share of returns captured. By extending standard innovation models, it shows outcomes where appropriation is a decision variable, not a constant. _Strategic appropriation model_ | C12: **New Innovation Lens** – Contributes to theory by integrating strategy into the concept of appropriability: entrepreneurs are not passive takers of profit conditions; they actively manage them. This bridging perspective influences both theory (innovation models incorporating strategy) and practice (urging firms to invest in appropriability-enhancing activities). _Shaping the rewards of innovation_ | | **Hellmann (2007)** – _The role of patents for bridging the science–market gap_ (JEBO) | A12: **Science–Market Disconnect** – Many scientific discoveries struggle to reach market because knowledge is hard to transfer and appropriate. This institutional gap is the anomaly: significant innovations languish in labs without mechanisms to bridge to commercialization. _Valley of death_ | D12: **Bridge Mechanism** – Need tools (especially patents) to connect inventors with entrepreneurs/companies. Patents serve as a bridge by codifying knowledge and providing tradable rights, aligning incentives between scientists and commercializers. _Linking invention to venture_ | G1: **Theoretical Model** – Proposes a model where patenting an invention improves the odds of it being commercialized by making transactions feasible (e.g. reducing uncertainty for investors or firms). It likely formalizes how a patent signal or right increases investment in an idea. _Patent bridging model_ | C12: **Policy Insight** – Argues that patents facilitate technology transfer, turning scientific advances into market products. The contribution bridges innovation policy and entrepreneurship: strong IP regimes can **effectively** increase commercialization of science (but excessive friction impedes it). This guides policy toward fostering patenting in academia and encouraging licensing. _Patents as commercialization bridges_ | | **Hellmann & Puri (2000)** – _The interaction between product market and financing strategy: The role of venture capital_ (RFS) | A12: **Strategy–Finance Interplay** – Contrary to treating financing as independent, this study finds a nuanced anomaly: product market conditions (e.g. competition intensity) influence whether startups take VC money and how fast they go to market. Strategy and financing are entwined. _Competitive financing link_ | D12: **Synchronizing Funding & Strategy** – Founders must align financing choices with their competitive strategy. For example, in a fast-moving market, the need is to secure venture funding to scale quickly (outrun rivals), whereas in a slow market the need might be patience and control. _Financing–strategy alignment_ | G2: **Empirical & Model Combo** – Uses data on startups’ time-to-market and VC backing, and may present a model illustrating how competition affects the optimal financing path. Likely employs survival analysis or regressions showing faster launch with VC under high competition – capturing a dynamic decision. _Competition-driven financing model_ | C12: **Integrated Guidance** – Shows that venture financing isn’t one-size-fits-all: its value depends on market context. This contributes an integrated view that **efficient** startup success requires matching the financing strategy (VC vs bootstrapping) to product-market conditions. It informs entrepreneurs (and VCs) to consider market clockspeed in funding decisions. _Finance–market fit insight_ | | **Hsu (2006)** – _Venture capitalists and cooperative start-up commercialization strategy_ (Management Science) | A1: **VC-Driven Path** – Finds heterogeneity in commercialization: startups with reputable VCs often choose cooperative strategies (e.g. being acquired) rather than going it alone, even at lower valuation, which is counterintuitive (why accept less?). The anomaly is the influence of VC backing on strategy choice. _VC effect on exits_ | D12: **Exit Strategy Alignment** – Need to align startup strategy with investor goals and networks. A founder backed by VCs must weigh the benefit of a quicker, certain exit via sale (leveraging VC connections) against independent growth. Harmonizing these interests is key. _Investor–startup strategy fit_ | G1: **Signaling Model** – Proposes a model where VC endorsement signals startup quality to incumbents, facilitating earlier acquisition deals. The model and empirical evidence show prestigious VCs reduce information gaps, making cooperative commercialization (like acquisition) more viable for startups, albeit at a “discount” for speed. _VC signal and early exit model_ | C12: **Strategic VC Value** – Concludes that beyond capital, VCs add **strategic** value by opening the market for ideas (connections to acquirers) and accelerating time to liquidity. This bridges finance and strategy, highlighting that a top VC can be an efficient catalyst for cooperation, not just a financier. _VC as strategy catalyst_ | | **Lamoreaux & Sokoloff (2001)** – _Market trade in patents and the rise of specialized inventors in 19th-century USA_ (AER P&P) | A1: **Historical Specialization** – Documents that even in the 1800s, many inventors did not commercialize themselves but sold patents. This historical anomaly shows that specialized inventors thriving via patent trade is not just a modern phenomenon, challenging the view of the lone inventor-entrepreneur. _19thC patent marketplace_ | D12: **Institutional Support** – Need for institutions (patent system, brokers, patent attorneys) to enable inventors to sell inventions. The 19th-century experience underlines how legal and market infrastructure must develop to support a technology marketplace. _Infrastructure for idea trade_ | G0: **Archival Analysis** – Uses historical data (patent records, inventor careers) to infer the existence and scale of patent trading. No new formal model, but a narrative/empirical analysis showing patterns like serial inventors who repeatedly sold patents, indicating a functioning idea market. _Historical evidence study_ | C12: **Evolution of Innovation System** – Reveals that markets for ideas significantly contributed to innovation even in early industrialization, implying that efficient innovation systems often involve specialization and exchange. This insight bridges economic history with innovation theory, emphasizing that policies facilitating patent trade can foster inventive activity. _Origins of the idea market_ | | **Luo (2014)** – _When to sell your idea: Theory and evidence from the movie industry_ (Management Science) | A2: **Timing Trade-off** – Idea creators face a timing anomaly: sell an idea early (fast but cheap) or develop it further for a potentially bigger payoff later. This “when to sell” decision encapsulates a clockspeed dilemma under uncertainty. _Optimal exit timing_ | D12: **Staged Value Realization** – Need to manage a two-stage process: initial development (to increase value) versus timely exit. Creators must harmonize development efforts with market timing – too little development and buyers undervalue the idea; too much and they incur waste or miss the window. _Development vs. exit balance_ | G2: **Optimal Stopping Model** – Introduces a dynamic model (real options framework) of idea sale timing, calibrated with movie industry data. It derives conditions for selling now vs. later by weighing the gains from further development against risk of obsolescence or idea theft. _Idea sale timing model_ | C1: **Timing Strategy Guidance** – Delivers prescriptive insights: e.g. if incremental development greatly improves certainty or value, wait; if not, sell early. Thus it provides **effective** decision rules for innovators on maximizing returns. Empirically, using Hollywood sales data, it validates these rules (e.g. spec scripts with moderate development sell best). _When-to-sell rule_ | | **Malmendier & Lerner (2010)** – _Contractibility and the design of research agreements_ (AER) | A12: **Incomplete Contract Challenge** – In collaborative R&D, many outcomes are non-contractible (can’t be clearly measured or enforced), creating an anomaly where standard contracts fail. This leads to potential misaligned incentives between research partners. _Uncontractible R&D problem_ | D12: **Innovative Contracts** – Need for creative contract structures (milestones, equity splits, option-like provisions) to cope with unverifiable research progress. Parties must align goals despite the inability to write complete contracts, requiring trust and contingent agreements. _Milestone-based contracting_ | G1: **Contract Theory Model** – Develops a principal–agent model of a research partnership where some tasks/results can’t be specified upfront. An optimal contract is derived that uses available signals (like intermediate milestones or termination options) to incentivize innovation under limited contractibility. _Optimal R&D contract design_ | C2: **Efficient Collaboration Design** – Offers theoretical guidance on structuring R&D deals to maximize joint payoffs under contracting limits. By identifying mechanisms to mitigate moral hazard and hold-up, it contributes to more **efficient** industry-science collaborations and informs policy on how legal frameworks could improve research contractibility. _Designing efficient R&D deals_ | | **Teece (1986)** – _Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy_ (Research Policy) | A0: **Who Profits Paradox** – Poses the fundamental anomaly that innovating firms often fail to capture the profits from their own innovations. Instead, imitators or owners of complementary assets sometimes win, contradicting the expectation that creators profit most. _Innovator’s dilemma (profits)_ | D12: **Complementary Asset Need** – Innovators must secure access to complementary assets (manufacturing, distribution, etc.) or appropriate regimes to profit. This creates a strategic need either to integrate (do it yourself) or collaborate/license – aligning innovation with the asset holders. _Asset–innovation alignment_ | G12: **Integrative Framework** – Introduces a qualitative framework (no heavy math) classifying complementary assets (generic, specialized, co-specialized) and analyzing how appropriability regimes interact with asset distribution. It’s a mapping of scenarios rather than a formal model, effectively bridging economics and strategy. _Complementary asset matrix_ | C12: **Theory of Innovation Value Capture** – Provides a seminal contribution: a comprehensive model of how and when innovators can profit, integrating factors of IP strength and asset control. This framework has broad prescriptive reach – guiding managers to either build, borrow, or buy complementary assets – and shaped subsequent theory on **effective** innovation commercialization and **efficient** industry structure for innovation. _Profiting from innovation framework_ |