Study helps start-ups choose best activities to succeed through milestones.
problem Navigating multiple milestones in multi-activity start-ups.
method Stochastic control model with multiple controls, optimal policy analysis.
result Optimal strategies depend on riskiness and cost-effectiveness.
The DAO Report led to a significant shift of ICO activity to Europe.
problem The impact of U.S. regulatory changes on global ICO activity.
method Analysis of a global dataset of ICOs from 2014 to 2021, focusing on the DAO Report's effects.
result A substantial and persistent reallocation of ICO activity to Europe following the DAO Report.
Study shows registration regime improves IPO pricing efficiency in China.
problem Improving IPO pricing efficiency in China's entrepreneurial boards.
method Examined the impacts of regime changes and rule implementations on IPO pricing efficiency.
result The registration regime is most efficient in Chinese IPO pricing.
The present research aims to highlight the main factors influencing the development of entrepreneurial innovation in a rural environment and to perform an empirical study with the purpose of assessing the main problems in rural development. The research performed is mostly of a quantitative nature, being based on the u…
This paper compares token and equity financing for startups.
problem Understanding differences in return rates between token and equity financing.
method Developed a three-period model to analyze liquidity and return differences.
result Entrepreneurs can achieve higher payoffs by issuing tokens, especially for risk-averse investors with liquidity needs.
After the shocking series of bankruptcies started in 2008, the public does not trust anymore the classical methods of assessing business risks. The global economic severe downturn caused demand for both developed and emerging economies' exports to drop and the crisis became truly global. However, this current crisis of…
The paper analyzes how mutable blockchain protocols affect miner behavior and strategic stability.
problem The mutability of blockchain protocols undermines long-term planning and cooperative equilibria.
method Integrates Austrian capital theory with repeated game theory to examine miner behavior under different institutional conditions.
result Effective time preference increases when protocol rules are mutable, leading to political rent-seeking and undermining strategic coherence.
After September 2008, the advanced economies severe decline caused demand for emerging economies' exports to drop and the crisis became truly global, much deeper and broader than expected. In these times of global depression, most countries and companies are affected, some more than others. The financial crisis has tur…
In many developing countries intellectual property infringement and the commerce of pirate goods is an entrepreneurial activity. Digital piracy is very often the only media for having access to music, cinema, books and software. At the same time, bio-prospecting and infringement of indigenous knowledge rights by intern…
Proposes a method to create fair ITRs that balance value and fairness.
problem Fairness issues in ITRs that can lead to unfair advantages or disadvantages.
method Optimal transport theory to transform optimal ITRs into fair ITRs.
result Established a theoretical upper bound on value loss for improved trade-off ITRs.
Study on women entrepreneurs' access to finance in France.
problem Inequalities in accessing external finance for women entrepreneurs in France.
method Quantitative approach using data from a representative sample of women entrepreneurs.
result Founder status affects access to external finance; increases success in fundraising but reduces bank finance.
This review explores causal decision-making to improve decision quality.
problem Effective decision-making requires understanding causal relationships.
method Causal structure learning, causal effect learning, and causal policy learning.
result Challenges in causal decision-making are identified and recent advances are discussed.
Framework for robust decision making in changing environments with privacy constraints.
problem Interactive decision making in changing environments with constraints.
method Hybrid Decision Making with Structured Observations (hybrid DMSO) framework, local differentially private decision making, query-based learning, robust and smooth decision making.
result Strong connections and bounds derived for DEC, SQ dimension, local minimax complexity, learnability, and joint differential privacy.
Orchestrating the Twin Transition in GBS: A Socio-Technical Framework
problem MNCs' need to harmonize digital efficiency with environmental stewardship
method Technology Roadmapping (TRM) with ICT-centric innovation ecosystem toolkit
result Central 'operational airlock' for GBS in harmonizing digital and environmental transformation
Study minimax-optimal rates for offline decision-making with function approximation.
problem Statistical complexity of offline decision-making with function approximation.
method Near minimax-optimal rates for stochastic contextual bandits and Markov decision processes, using pseudo-dimension and behavior policy.
result Established performance limits and new characterization of behavior policy.
The paper tackles individualized decision-making under unmeasured confounding, providing a novel minimax solution and a paradox.
problem Unmeasured confounding in causal inference leads to biased estimates and affects individualized decision-making.
method The authors establish a formal link between individualized decision-making under partial identification and classical decision theory, providing a minimax solution and a paradox.
result A novel minimax solution for individualized decision-making/policy assignment is provided, and an interesting paradox is drawn.
Deep learning enhances business analytics and operations research performance.
problem Scarcity of deep learning research in business analytics and operations research.
method Review and analysis of existing literature, computational experiments, case studies.
result Deep neural networks improve operational performance in business analytics and operations research.
This paper develops a framework for efficient decision-making under time pressure.
problem Efficient decision-making under time pressure and subjective tradeoffs.
method Unified framework for evidence-based decision-making under time pressure.
result Ability to model and understand decision-making behavior under time constraints.
New active learning strategy improves decision-making accuracy.
problem Maximizing decision-making accuracy in sequential data acquisition.
method Introduces a novel active learning criterion that maximizes expected information gain on the posterior decision distribution.
result Improved performance in decision-making accuracy compared to existing alternatives.
New algorithms for fast online decision making using neural networks and martingale posteriors.
problem Online sequential decision making under uncertainty.
method Martingale posterior neural networks for fast online learning and decision making.
result Achieves competitive performance-speed trade-offs in non-stationary contextual bandits and Bayesian optimization.
Paper tackles risk-sensitive decision-making under uncertainty.
problem Risk-sensitive decision-making problem under uncertainty.
method Formulated as a stochastic control problem, delineated necessary optimality conditions.
result Illustrative examples from optimal betting and inventory management support the theory.
Machine learning attacks mimic cellular decision-making, revealing new defense mechanisms.
problem Adversarial perturbations fool machine learning models, similar to how ligands prevent correct signaling in cells.
method Formal analogy between neural networks and cellular decision-making models, applying machine learning techniques to study cellular processes.
result Found two regimes in cellular decision-making models, each with a critical point that shapes the loss landscape and defense mechanisms.
IRL models human risk decisions based on past outcomes.
problem Understanding human risk decisions under risk.
method Inverse Reinforcement Learning (IRL) with features reflecting state history.
result Human reward function explains risk-prone and risk-averse decisions.
A rational model explains why humans overestimate extreme events in decisions.
problem Human tendency to overestimate extreme outcomes in decisions.
method Formal metacognitive model based on resource availability.
result Empirical evidence confirms the model's predictions.
Optimizes decision-making with variational Bayesian methods for continuous utilities.
problem Inference approximations for continuous utilities without full posterior knowledge.
method Automatic pipeline that co-opts continuous utilities into variational inference algorithms.
result Consistent improvement in decision-making when calibrating approximations for specific utilities.
Three ways synchronization in financial markets can cause contagion, using models of decision-making and oscillators.
problem Contagion in financial markets caused by synchronization of decision-making.
method Agent-based modeling, integrate-and-fire oscillators, and communication models.
result Synchronization in financial markets can lead to turbulent periods and contagion.
Corrects approximate Bayesian inference for better decision-making.
problem Sub-optimal decisions due to inaccurate posterior predictive distributions.
method Trains a separate model to correct decision-making under approximate posterior, combining Bayesian modeling with optimization.
result Empirically demonstrates improved predictive accuracy in various problems.
Develops methods for finding counterfactual explanations in sequential decision making.
problem Finding counterfactual explanations for sequential decision making processes.
method Formal characterization of sequential actions and states using Markov decision processes and Gumbel-Max structural causal model. Introduces a polynomial time algorithm based on dynamic programming.
result Algorithm finds optimal counterfactual explanations for sequential decision making.
This review covers methods for autonomous driving including tracking, prediction, and decision making.
problem Improving autonomous driving through better tracking, prediction, and decision making.
method Approaches based on neural networks, stochastic techniques, and reinforcement learning are discussed.
result Effective methods for autonomous driving are identified and compared.
Study shows how AI model can improve decision-making with missing data.
problem Sequential decision-making with missing covariates.
method Introduced model elasticity to quantify imputation discrepancy; used statistical learning and regression for calibration.
result Calibrating pre-trained models can significantly reduce regret in decision-making.
Improving cancer treatment decisions requires considering causal effects, not just model accuracy.
problem Cancer outcome prediction models may cause harm when used for treatment decisions.
method Explains the importance of considering causal effects in model validation and provides guidelines.
result Building and validating models that are useful for decision making requires considering causal effects.
Predicting startup success using Crunchbase data and deep learning.
problem Predicting startup success in a volatile entrepreneurial ecosystem.
method Novel deep learning model integrating funding metrics, founder features, and industry category.
result Achieved 14 times capital growth and identified high-potential startups.
Proposes a recourse algorithm for machine learning decisions.
problem Individuals can suffer unfair outcomes in black-box systems.
method Models data distribution, generates smallest changes for improvement.
result Algorithm applicable to supervised and causal systems.
Active learning improves decision-making from imbalanced observational data.
problem Reliability of prediction-based decisions in imbalanced observational data.
method Estimate Type S error rate to assess reliability, use active learning to collect new data.
result Active learning improves decision-making reliability in imbalanced data.
Paper offers a fast convergence theory for offline decision making.
problem Offline decision making problems, including reinforcement learning and off-policy evaluation.
method Introduces a framework (DMOF) and algorithm (EDD) with a fast convergence guarantee.
result Demonstrates a fast convergence guarantee with a lower bound complement.
Conformal prediction sets improve human decision making by quantifying model uncertainty.
problem Humans signal uncertainty and offer alternatives when unsure, but machine learning models often lack this feature.
method Conducted a randomized controlled trial with human subjects given conformal prediction sets.
result Human accuracy improves when given conformal prediction sets compared to fixed-size prediction sets.
Study on multi-agent decision making complexity, showing sample efficiency gaps.
problem Understanding sample efficiency in multi-agent decision making.
method General framework for interactive decision making, focusing on equilibrium computation.
result No 'reasonable' complexity measure can close gaps between single and multiple agents.
With an eye towards human-centered automation, we contribute to the development of a systematic means to infer features of human decision-making from behavioral data. Motivated by the common use of softmax selection in models of human decision-making, we study the maximum likelihood parameter estimation problem for sof…
Unified framework for sequential decision making using meta-learning surrogate models.
problem Sequential decision making problems in various domains.
method Probabilistic model-based approach with meta-learning for data-efficient adaptation.
result Efficient and general black-box learning approach across different problem domains.
Discretizing time series data introduces bias for decision-making models.
problem Discretization of non-regular time series data introduces bias in decision-making models.
method Showed discretization bias and proposed using continuous-time models instead.
result Avoiding discretization introduces unbiased decision-making models.
Study on information evolution in interactive decision making using multi-armed bandits.
problem Understanding information dynamics in interactive decision making.
method Stochastic multi-armed bandit problem, focusing on optimal arm with a fixed margin.
result Distinct growth phases in mutual information, showing decoupling between success probability and information gain.
Unified framework models neural decision-making, improving accuracy.
problem Limitations in modeling neural activity during decision-making.
method Unifying framework based on state-space models with scalable inference.
result Two-dimensional accumulator better captures neural responses.
New methods improve robust decision-making under uncertainty in off-policy evaluation.
problem Statistical uncertainty and causal considerations in off-policy evaluation.
method Marginal Ratio (MR) estimator, Conformal Off-Policy Prediction (COPP), causal bounds.
result Improved robustness and uncertainty quantification in off-policy decision-making.
InvestorBench benchmarks LLM agents in financial tasks.
problem Lack of a comprehensive benchmark for LLM-based financial agents.
method Developed a benchmark with diverse financial tasks and datasets.
result Evaluated LLM agents' performance across various financial products and market environments.
A new framework designs experiments for better decision-making.
problem Suboptimal experimental designs for downstream decision-making.
method Amortized decision-aware Bayesian Experimental Design (BED) with Transformer Neural Decision Process (TNDP).
result TNDP effectively designs experiments and facilitates accurate decision-making.
Unified framework for human-like decision making in various sequential tasks.
problem Real-life decision-making involves diverse strategies leading to similar outcomes.
method Two-stream reward processing mechanism for flexible and unified models.
result Framework unified MAB, CB, and RL with comparable performance.
A new fairness metric for decision-making algorithms, conditioning on known fair variables.
problem Fairness issues in decision-making systems.
method Conditional fairness metric, Derivable Conditional Fairness Regularizer (DCFR), adversarial representation.
result Traditional fairness notations are special cases of the new conditional fairness notation.
The paper investigates how ambiguous data and cognitive biases affect machine learning in humanitarian decision making.
problem Ambiguous data and cognitive biases impact the interpretability of machine learning models in humanitarian decision making.
method The study will explore the effects of data ambiguity and cognitive biases on machine learning algorithms in humanitarian contexts.
result The research aims to uncover the specific ways in which ambiguous data and cognitive biases influence the interpretability of machine learning models in humanitarian decision making.