A Multi-Period Game Theoretic Model of Venture Capitalists and Entrepreneurs
Bibliographic record
Abstract
As venture capitalists (VCs) have an important role in financial start-ups, most studies have focused on venture capital as a short-term source of financing. The authors examine the relation between a VC and an entrepreneur in a multi-period game in which the contract is set at the beginning of the game, and follows the relation from its start to the end. This multi-period game theoretic model focuses on the problem of moral hazard – the entrepreneur's hidden effort and how a VC can cope with it, using staged investments. First a game between a VC and an entrepreneur is presented, in which the VC is unable to notice the entrepreneur’s effort. Next, a multi-period game between the parties is examined, where the contract is established at the beginning of the multi-period game. The results of the model are presented, and several propositions and theorems are advanced. The multi-period aspects of the model allow for deriving the strategic behavior of the VCs and entrepreneurs over time. The model is consistent with reality, where the average duration of the relation between venture capitalists and entrepreneurs is several years and the investment is made in stages. The study provides insights on optimal contracts and the characterization of an endogenous exit point. It is suggested that the optimal incentive scheme back load all incentive payments to the entrepreneur, and a straight debt contract is optimal in venture financing. Recommendations are made for further studies on the contracts among VCs who syndicate together, as well as the bargaining between the VC and the entrepreneur.(CBS)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".