Strategies for Milestone-driven Start-ups in Multi-activity Settings
- URL: http://arxiv.org/abs/2607.27563v1
- Date: Thu, 30 Jul 2026 01:16:21 GMT
- Title: Strategies for Milestone-driven Start-ups in Multi-activity Settings
- Abstract summary: We investigate the strategies for start-ups in a milestone-oriented setting.<n>We use a model of an entrepreneurial start-up firm, where its state is captured by a diffusion process.<n>Depending on whether the process reaches a fixed upper boundary or a lower one, the start-up firm succeeds or fails.
- Score: 2.055949720959582
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: New venture start-ups need to ``survive'' through multiple stages of reaching milestone targets. We investigate the strategies for start-ups in a milestone-oriented setting. We examine a model of an entrepreneurial start-up firm, where its state is captured by a diffusion process. The entrepreneur can choose between multiple activities (or controls), which incur different cost and determine the drift and the variance of the process. Depending on whether the process reaches a fixed upper boundary or a lower one, the start-up firm succeeds or fails. Continuous-time stochastic models with multiple ($\ge 3$) controls are typically very challenging to deal with. In this work, we are able to completely solve for the optimal policy and provide an explicit characterization of its structure. In particular, the optimal policy only uses controls from a set characterized by a so-called efficient frontier curve that orders the controls by two intuitive measures: riskiness (drift-to-volatility ratio) and cost-effectiveness (drift-to-cost ratio). A unique feature of our model is that depending on the model parameters, the efficient frontier curves can be of different types, resulting in qualitatively different structures of the optimal policy. As far as we know, this is the first study that analyzes a stochastic control model which admits efficient frontier curves of different types. Our work provides start-up firms with intuitive measures to evaluate their activities and offers valuable insights on how the optimal strategies in a milestone-oriented setting change qualitatively contingent upon the specific scenario. We believe the results provide a foundational block in the study of entrepreneurial decision-making.
Related papers
- Before Thinking, Learn to Decide: Proactive Routing for Efficient Visual Reasoning [87.18590662029432]
We propose PRP, a Proactive Routing Paradigm that enables early decision-making by jointly evaluating the competence of both the draft and target models.<n>Our draft Rating Learning (DRL) equips the draft model with an internal confidence estimator, while Joint Rating Learning (JRL) predicts how well the target model can handle a given query.<n>These ratings enable fine-grained, instance-level textbfProactive Routing and substantially accelerate inference without compromising overall performance.
arXiv Detail & Related papers (2026-06-29T12:30:24Z) - Selective Ensemble Based on Preference-Directed Multi-Objective Bandits [90.75513823660775]
We formalize a sequential decision problem under partially specified linear preferences.<n>We then propose a preference-directed upper confidence bound (PrefUCB) algorithm.<n> Experiments on large pre-trained model selective ensemble tasks and online asset allocation under institutional mandates validate our method.
arXiv Detail & Related papers (2026-06-20T07:52:46Z) - Budget-Aware Agentic Routing via Boundary-Guided Training [24.0709108941881]
Budget-Aware Agentic Routing selects between a cheap and an expensive model at each step to optimize the cost-success frontier.<n> Boundary-Guided Training builds a difficulty taxonomy to anchor learning under sparse rewards.<n>Experiment results show that our method improves the efficiency frontier, matching strong routing baselines at substantially lower cost.
arXiv Detail & Related papers (2026-02-04T07:39:27Z) - Decision Flow Policy Optimization [53.825268058199825]
We show that generative models can effectively model complex multi-modal action distributions and achieve superior robotic control in continuous action spaces.<n>Previous methods usually adopt the generative models as behavior models to fit state-conditioned action distributions from datasets.<n>We propose Decision Flow, a unified framework that integrates multi-modal action distribution modeling and policy optimization.
arXiv Detail & Related papers (2025-05-26T03:42:20Z) - A Unified Approach to Routing and Cascading for LLMs [5.653106385738822]
Large language models (LLMs) embedded in various agentic systems have increased the potential of model selection strategies to improve the cost-performance tradeoff.<n>Existing strategies involve either routing, where a single model is chosen per query, or cascading, which sequentially runs increasingly larger models until a satisfactory answer is found.<n>We derive a novel optimal strategy for cascading and prove the optimality of an existing routing strategy.<n>We propose cascade routing, a unified framework that integrates routing and cascading into a theoretically optimal strategy.
arXiv Detail & Related papers (2024-10-14T10:00:49Z) - Model-Free Active Exploration in Reinforcement Learning [53.786439742572995]
We study the problem of exploration in Reinforcement Learning and present a novel model-free solution.
Our strategy is able to identify efficient policies faster than state-of-the-art exploration approaches.
arXiv Detail & Related papers (2024-06-30T19:00:49Z) - Risk-Controlling Model Selection via Guided Bayesian Optimization [35.53469358591976]
We find a configuration that adheres to user-specified limits on certain risks while being useful with respect to other conflicting metrics.
Our method identifies a set of optimal configurations residing in a designated region of interest.
We demonstrate the effectiveness of our approach on a range of tasks with multiple desiderata, including low error rates, equitable predictions, handling spurious correlations, managing rate and distortion in generative models, and reducing computational costs.
arXiv Detail & Related papers (2023-12-04T07:29:44Z) - Latent State Marginalization as a Low-cost Approach for Improving
Exploration [79.12247903178934]
We propose the adoption of latent variable policies within the MaxEnt framework.
We show that latent variable policies naturally emerges under the use of world models with a latent belief state.
We experimentally validate our method on continuous control tasks, showing that effective marginalization can lead to better exploration and more robust training.
arXiv Detail & Related papers (2022-10-03T15:09:12Z) - Evaluating model-based planning and planner amortization for continuous
control [79.49319308600228]
We take a hybrid approach, combining model predictive control (MPC) with a learned model and model-free policy learning.
We find that well-tuned model-free agents are strong baselines even for high DoF control problems.
We show that it is possible to distil a model-based planner into a policy that amortizes the planning without any loss of performance.
arXiv Detail & Related papers (2021-10-07T12:00:40Z) - Optimal Bidding Strategy without Exploration in Real-time Bidding [14.035270361462576]
maximizing utility with a budget constraint is the primary goal for advertisers in real-time bidding (RTB) systems.
Previous works ignore the losing auctions to alleviate the difficulty with censored states.
We propose a novel practical framework using the maximum entropy principle to imitate the behavior of the true distribution observed in real-time traffic.
arXiv Detail & Related papers (2020-03-31T20:43:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.