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Abstract
Learning effective pricing strategies is crucial in digital marketplaces, especially when buyers' valuations are unknown and must be inferred through interaction. We study the online contextual pricing problem, where a seller observes a stream of context-valuation pairs and dynamically sets prices. Moreover, departing from traditional online learning frameworks, we consider a strategic setting in which buyers may misreport valuations to influence future prices, a challenge known as strategic overfitting (Amin et al., 2013). We introduce a strategy-robust notion of regret for multi-buyer online environments, capturing worst-case strategic behavior in the spirit of the Price of Anarchy. Our first contribution is a polynomial-time approximation scheme (PTAS) for learning linear pricing policies in adversarial, adaptive environments, enabled by a novel online sketching technique. Building on this result, we propose our main construction: the Sparse Update Mechanism (SUM), a simple yet effective sequential mechanism that ensures robustness to all Nash equilibria among buyers. Moreover, our construction yields a black-box reduction from online expert algorithms to strategy-robust learners.
Learning effective pricing strategies is crucial in digital marketplaces, especially when buyers' valuations are unknown and must be inferred through interaction. We study the online contextual pricing problem, where a seller observes a stream of context-valuation pairs and dynamically sets prices. Moreover, departing from traditional online learning frameworks, we consider a strategic setting in which buyers may misreport valuations to influence future prices, a challenge known as strategic overfitting (Amin et al., 2013). We introduce a strategy-robust notion of regret for multi-buyer online environments, capturing worst-case strategic behavior in the spirit of the Price of Anarchy. Our first contribution is a polynomial-time approximation scheme (PTAS) for learning linear pricing policies in adversarial, adaptive environments, enabled by a novel online sketching technique. Building on this result, we propose our main construction: the Sparse Update Mechanism (SUM), a simple yet effective sequential mechanism that ensures robustness to all Nash equilibria among buyers. Moreover, our construction yields a black-box reduction from online expert algorithms to strategy-robust learners.
Why we think this paper is great for you:
This paper directly addresses learning effective pricing strategies in dynamic environments. You will find its focus on online contextual pricing particularly valuable for understanding advanced pricing mechanisms.
This paper directly addresses learning effective pricing strategies in dynamic environments. You will find its focus on online contextual pricing particularly valuable for understanding advanced pricing mechanisms.
AI Summary
- The Unconstrained method produces the least accurate results for all three options (barrier-call, Equinox, and full Equinox). [3]
- Control-variate: A method that uses a control variable to adjust the option's price and hedge. [3]
- The Unconstrained method is less accurate than methods that incorporate the payoff condition for all three options (barrier-call, Equinox, and full Equinox). [3]
- Methods that incorporate the payoff condition yield a more accurate price and a better hedge. [2]
- For the pure barrier-call component of the Equinox option, the standard deviation for the Unconstrained method is 10% to 14% higher than other methods. [1]
Abstract
In incomplete financial markets, pricing and hedging European options lack a unique no-arbitrage solution due to unhedgeable risks. This paper introduces a constrained deep learning approach to determine option prices and hedging strategies that minimize the Profit and Loss (P&L) distribution around zero. We employ a single neural network to represent the option price function, with its gradient serving as the hedging strategy, optimized via a loss function enforcing the self-financing portfolio condition. A key challenge arises from the non-smooth nature of option payoffs (e.g., vanilla calls are non-differentiable at-the-money, while digital options are discontinuous), which conflicts with the inherent smoothness of standard neural networks. To address this, we compare unconstrained networks against constrained architectures that explicitly embed the terminal payoff condition, drawing inspiration from PDE-solving techniques. Our framework assumes two tradable assets: the underlying and a liquid call option capturing volatility dynamics. Numerical experiments evaluate the method on simple options with varying non-smoothness, the exotic Equinox option, and scenarios with market jumps for robustness. Results demonstrate superior P&L distributions, highlighting the efficacy of constrained networks in handling realistic payoffs. This work advances machine learning applications in quantitative finance by integrating boundary constraints, offering a practical tool for pricing and hedging in incomplete markets.
In incomplete financial markets, pricing and hedging European options lack a unique no-arbitrage solution due to unhedgeable risks. This paper introduces a constrained deep learning approach to determine option prices and hedging strategies that minimize the Profit and Loss (P&L) distribution around zero. We employ a single neural network to represent the option price function, with its gradient serving as the hedging strategy, optimized via a loss function enforcing the self-financing portfolio condition. A key challenge arises from the non-smooth nature of option payoffs (e.g., vanilla calls are non-differentiable at-the-money, while digital options are discontinuous), which conflicts with the inherent smoothness of standard neural networks. To address this, we compare unconstrained networks against constrained architectures that explicitly embed the terminal payoff condition, drawing inspiration from PDE-solving techniques. Our framework assumes two tradable assets: the underlying and a liquid call option capturing volatility dynamics. Numerical experiments evaluate the method on simple options with varying non-smoothness, the exotic Equinox option, and scenarios with market jumps for robustness. Results demonstrate superior P&L distributions, highlighting the efficacy of constrained networks in handling realistic payoffs. This work advances machine learning applications in quantitative finance by integrating boundary constraints, offering a practical tool for pricing and hedging in incomplete markets.
Why we think this paper is great for you:
This paper introduces a deep learning approach to determine option prices and hedging strategies. It offers insights into applying advanced AI techniques for complex pricing challenges.
This paper introduces a deep learning approach to determine option prices and hedging strategies. It offers insights into applying advanced AI techniques for complex pricing challenges.
AI Summary
- An optimised deep learning model for dynamic market behaviour prediction has been proposed. [3]
- The model performs better at capturing market complexity and uncertainty than traditional methods such as ARIMA, SVR, and random forest. [3]
- The integration of advanced neural network architecture and reinforcement learning methods enables the model to demonstrate enhanced efficiency in resource allocation and profit maximisation. [3]
- Further research should concentrate on enhancing the scalability and interpretability of the models in order to facilitate their use with larger and more diverse datasets. [3]
- Entropy: A measure of the complexity and uncertainty inherent in market behaviour. [3]
- Learning Rate: The rate at which the model converges during training. [3]
- The proposed deep learning model has been shown to outperform traditional methods such as ARIMA, SVR, and random forest in terms of predictive power. [3]
- The integration of sophisticated reinforcement learning techniques, such as multi-agent systems or meta-learning, is anticipated to enhance the models' capacity to adapt to the dynamic nature of evolving markets. [2]
- Maximum Iterations: The maximum number of iterations allowed for the model to converge. [1]
Abstract
The advent of financial technology has witnessed a surge in the utilization of deep learning models to anticipate consumer conduct, a trend that has demonstrated considerable potential in enhancing lending strategies and bolstering market efficiency. We study multi-horizon demand forecasting on e-commerce transactions using the UCI Online Retail II dataset. Unlike prior versions of this manuscript that mixed financial-loan narratives with retail data, we focus exclusively on retail market behavior and define a clear prediction target: per SKU daily demand (or revenue) for horizons H=1,7,14. We present a hybrid sequence model that combines multi-scale temporal convolutions, a gated recurrent module, and time-aware self-attention. The model is trained with standard regression losses and evaluated under MAE, RMSE, sMAPE, MASE, and Theil's U_2 with strict time-based splits to prevent leakage. We benchmark against ARIMA/Prophet, LSTM/GRU, LightGBM, and state-of-the-art Transformer forecasters (TFT, Informer, Autoformer, N-BEATS). Results show consistent accuracy gains and improved robustness on peak/holiday periods. We further provide ablations and statistical significance tests to ensure the reliability of improvements, and we release implementation details to facilitate reproducibility.
The advent of financial technology has witnessed a surge in the utilization of deep learning models to anticipate consumer conduct, a trend that has demonstrated considerable potential in enhancing lending strategies and bolstering market efficiency. We study multi-horizon demand forecasting on e-commerce transactions using the UCI Online Retail II dataset. Unlike prior versions of this manuscript that mixed financial-loan narratives with retail data, we focus exclusively on retail market behavior and define a clear prediction target: per SKU daily demand (or revenue) for horizons H=1,7,14. We present a hybrid sequence model that combines multi-scale temporal convolutions, a gated recurrent module, and time-aware self-attention. The model is trained with standard regression losses and evaluated under MAE, RMSE, sMAPE, MASE, and Theil's U_2 with strict time-based splits to prevent leakage. We benchmark against ARIMA/Prophet, LSTM/GRU, LightGBM, and state-of-the-art Transformer forecasters (TFT, Informer, Autoformer, N-BEATS). Results show consistent accuracy gains and improved robustness on peak/holiday periods. We further provide ablations and statistical significance tests to ensure the reliability of improvements, and we release implementation details to facilitate reproducibility.
Why we think this paper is great for you:
This paper explores the optimization of deep learning models for anticipating consumer behavior. Its insights into multi-horizon demand forecasting are directly applicable to your work.
This paper explores the optimization of deep learning models for anticipating consumer behavior. Its insights into multi-horizon demand forecasting are directly applicable to your work.
AI Summary
- Behavioral backdoor detection in AI models is a critical problem that cannot be solved model by model due to the 43.4% generalization gap. [3]
- LLM: Large Language Model AI supply chain: The network of organizations involved in the development and deployment of AI models Backdoor attack: A type of attack where an adversary injects a malicious function into a model, allowing them to manipulate its behavior Data poisoning: The process of manipulating training data to cause a model to produce incorrect or biased results Sleeper agent: A type of backdoor attack that involves training a model to behave normally during testing but to produce different outputs when deployed in the wild Neural cleanse: A technique for identifying and mitigating backdoor attacks in neural networks Activation clustering: A method for detecting backdoor attacks by analyzing the activation patterns of a model's neurons [3]
- Model-aware detection offers a practical path forward, achieving 90.6% universal accuracy across heterogeneous LLM ecosystems. [2]
- The cross-LLM backdoor detection problem is now well-characterized, and our findings provide a foundation for building robust defenses against this critical threat. [1]
Abstract
As AI agents become integral to enterprise workflows, their reliance on shared tool libraries and pre-trained components creates significant supply chain vulnerabilities. While previous work has demonstrated behavioral backdoor detection within individual LLM architectures, the critical question of cross-LLM generalization remains unexplored, a gap with serious implications for organizations deploying multiple AI systems. We present the first systematic study of cross-LLM behavioral backdoor detection, evaluating generalization across six production LLMs (GPT-5.1, Claude Sonnet 4.5, Grok 4.1, Llama 4 Maverick, GPT-OSS 120B, and DeepSeek Chat V3.1). Through 1,198 execution traces and 36 cross-model experiments, we quantify a critical finding: single-model detectors achieve 92.7% accuracy within their training distribution but only 49.2% across different LLMs, a 43.4 percentage point generalization gap equivalent to random guessing. Our analysis reveals that this gap stems from model-specific behavioral signatures, particularly in temporal features (coefficient of variation > 0.8), while structural features remain stable across architectures. We show that model-aware detection incorporating model identity as an additional feature achieves 90.6% accuracy universally across all evaluated models. We release our multi-LLM trace dataset and detection framework to enable reproducible research.
As AI agents become integral to enterprise workflows, their reliance on shared tool libraries and pre-trained components creates significant supply chain vulnerabilities. While previous work has demonstrated behavioral backdoor detection within individual LLM architectures, the critical question of cross-LLM generalization remains unexplored, a gap with serious implications for organizations deploying multiple AI systems. We present the first systematic study of cross-LLM behavioral backdoor detection, evaluating generalization across six production LLMs (GPT-5.1, Claude Sonnet 4.5, Grok 4.1, Llama 4 Maverick, GPT-OSS 120B, and DeepSeek Chat V3.1). Through 1,198 execution traces and 36 cross-model experiments, we quantify a critical finding: single-model detectors achieve 92.7% accuracy within their training distribution but only 49.2% across different LLMs, a 43.4 percentage point generalization gap equivalent to random guessing. Our analysis reveals that this gap stems from model-specific behavioral signatures, particularly in temporal features (coefficient of variation > 0.8), while structural features remain stable across architectures. We show that model-aware detection incorporating model identity as an additional feature achieves 90.6% accuracy universally across all evaluated models. We release our multi-LLM trace dataset and detection framework to enable reproducible research.
Why we think this paper is great for you:
This paper investigates vulnerabilities in AI agent supply chains, a critical area for ensuring the security and reliability of your AI-driven operations. You will appreciate its focus on the practical challenges of integrating AI into supply chain workflows.
This paper investigates vulnerabilities in AI agent supply chains, a critical area for ensuring the security and reliability of your AI-driven operations. You will appreciate its focus on the practical challenges of integrating AI into supply chain workflows.
AI Summary
- The proposed solution involves constructing a line digraph L(D) from a given graph D that represents the users' preferences. [3]
- The line digraph is used to determine the optimal prices for each time slot. [3]
- Line digraph: A graph constructed from a given graph by replacing each vertex with a path of length equal to the degree of the original vertex. [3]
- The paper presents a graph-based solution for the time slot pricing problem in transportation systems. [2]
Abstract
A company provides a service at different time slots, each slot being endowed with a capacity. A non-atomic population of users is willing to purchase this service. The population is modeled as a continuous measure over the preferred times. Every user looks at the time slot that minimizes the sum of the price assigned by the company to this time slot and the distance to their preferred time. If this sum is non-negative, then the user chooses this time slot for getting the service. If this sum is positive, then the user rejects the service. We address the problem of finding prices that ensure that the volume of users choosing each time slot is below capacity, while maximizing the revenue of the company. For the case where the distance function is convex, we propose an exact algorithm for solving this problem in time $O(n^3|P|^3)$, where $P$ is the set of possible prices and $n$ is the number of time slots. For the case where the prices can be any real numbers, this algorithm can also be used to find asymptotically optimal solutions in polynomial time under mild extra assumptions on the distance function and the measure modeling the population.
A company provides a service at different time slots, each slot being endowed with a capacity. A non-atomic population of users is willing to purchase this service. The population is modeled as a continuous measure over the preferred times. Every user looks at the time slot that minimizes the sum of the price assigned by the company to this time slot and the distance to their preferred time. If this sum is non-negative, then the user chooses this time slot for getting the service. If this sum is positive, then the user rejects the service. We address the problem of finding prices that ensure that the volume of users choosing each time slot is below capacity, while maximizing the revenue of the company. For the case where the distance function is convex, we propose an exact algorithm for solving this problem in time $O(n^3|P|^3)$, where $P$ is the set of possible prices and $n$ is the number of time slots. For the case where the prices can be any real numbers, this algorithm can also be used to find asymptotically optimal solutions in polynomial time under mild extra assumptions on the distance function and the measure modeling the population.
Why we think this paper is great for you:
This paper offers an exact method for time slot pricing, providing a foundational understanding of how to optimize service allocation. Its detailed approach to pricing mechanisms will be very useful to you.
This paper offers an exact method for time slot pricing, providing a foundational understanding of how to optimize service allocation. Its detailed approach to pricing mechanisms will be very useful to you.
AI Summary
- Multi-objective optimization: An approach that considers multiple criteria or objectives simultaneously to find the best solution. [3]
- The research develops a methodological framework for planning and operation of a Hydrogen Supply Chain (HSC) in Corsica, explicitly incorporating monthly fluctuations in hydrogen demand, the availability of renewable energy resources, and water constraints. [2]
Abstract
A multi-objective framework for hydrogen supply chain (HSC) planning is developed for island contexts, incorporating Mixed-Integer Linear Programming (MILP) over multiple time periods. The model minimizes total system cost, greenhouse gas (GHG) emissions, and a risk index criteria. The case study of Corsica is considered, using Geographic Information Systems (GIS) for spatial analysis and infrastructure locating. The 2050 future design of the HSC is determined including site selection, capacity sizing, and technology choices. The proposed m-TOPSIS-based multi objectives solution shows a decentralized infrastructure with a levelized cost of hydrogen of ___6.55/kg, and greenhouse gas emissions under 2 kgCO___e/kg H___. The study also integrates water availability and tourism-induced demand variation as key drivers of energy planning in insular regions.
A multi-objective framework for hydrogen supply chain (HSC) planning is developed for island contexts, incorporating Mixed-Integer Linear Programming (MILP) over multiple time periods. The model minimizes total system cost, greenhouse gas (GHG) emissions, and a risk index criteria. The case study of Corsica is considered, using Geographic Information Systems (GIS) for spatial analysis and infrastructure locating. The 2050 future design of the HSC is determined including site selection, capacity sizing, and technology choices. The proposed m-TOPSIS-based multi objectives solution shows a decentralized infrastructure with a levelized cost of hydrogen of ___6.55/kg, and greenhouse gas emissions under 2 kgCO___e/kg H___. The study also integrates water availability and tourism-induced demand variation as key drivers of energy planning in insular regions.
Why we think this paper is great for you:
This paper develops a multi-objective framework for supply chain planning, focusing on cost, emissions, and risk. You will find its approach to strategic supply chain development highly relevant.
This paper develops a multi-objective framework for supply chain planning, focusing on cost, emissions, and risk. You will find its approach to strategic supply chain development highly relevant.
AI Summary
- The text discusses leader-follower problems in the context of optimization and decision-making under uncertainty. [2]
- Follower: The player who responds to the leader's decisions. [1]
Abstract
Energy systems are changing rapidly. More and more, energy production is becoming decentralized, highly variable and intermittent (solar, wind), while demand is diversifying (electric vehicles). As a result, balancing supply and demand is becoming more complex, making the adjustment of demand an interesting tool. Demand response is a typical leader-follower problem: a consumer (follower) adjusts his energy consumption based on the prices (or any other incentive) set by the supplier (leader). We propose a versatile and modular framework to address any leader-follower problem, focusing on the handling of often overlooked informational issues. First, we introduce a model that defines the rules of the game (W-model): agents are decision-makers, and Nature encapsulates everything beyond their control, such as private knowledge and exogenous factors. Following the so-called Witsenhausen intrinsic model, we present an efficient way to represent - on a product set, equipped with a product $σ$-algebra - the information available to agents when making decisions. Next, we introduce Games in Product Form (W-games) by equipping each player (a group of agents) with preferences (objective function and belief) over different outcomes. Thereby, we incorporate an additional layer of information, the characteristics of the preferences linked to players, which affects the possible definitions of an equilibrium. We make this explicit in Nash and Stackelberg equilibria. Equipped with this framework, we reformulate several papers on demand response, highlighting overlooked informational issues. We also provide an application based on the Thailand demand response program.
Energy systems are changing rapidly. More and more, energy production is becoming decentralized, highly variable and intermittent (solar, wind), while demand is diversifying (electric vehicles). As a result, balancing supply and demand is becoming more complex, making the adjustment of demand an interesting tool. Demand response is a typical leader-follower problem: a consumer (follower) adjusts his energy consumption based on the prices (or any other incentive) set by the supplier (leader). We propose a versatile and modular framework to address any leader-follower problem, focusing on the handling of often overlooked informational issues. First, we introduce a model that defines the rules of the game (W-model): agents are decision-makers, and Nature encapsulates everything beyond their control, such as private knowledge and exogenous factors. Following the so-called Witsenhausen intrinsic model, we present an efficient way to represent - on a product set, equipped with a product $σ$-algebra - the information available to agents when making decisions. Next, we introduce Games in Product Form (W-games) by equipping each player (a group of agents) with preferences (objective function and belief) over different outcomes. Thereby, we incorporate an additional layer of information, the characteristics of the preferences linked to players, which affects the possible definitions of an equilibrium. We make this explicit in Nash and Stackelberg equilibria. Equipped with this framework, we reformulate several papers on demand response, highlighting overlooked informational issues. We also provide an application based on the Thailand demand response program.
Why we think this paper is great for you:
This paper explores modeling demand response to balance supply and demand in evolving energy systems. Its focus on adjusting demand will provide valuable perspectives for your interests.
This paper explores modeling demand response to balance supply and demand in evolving energy systems. Its focus on adjusting demand will provide valuable perspectives for your interests.
AI for Supply Chain Optimization
Abstract
We generalize the control function approach to production function estimation. Our generalization accommodates scenarios in which productivity evolves jointly with other unobservable factors such as latent demand shocks and the invertibility assumption underpinning the traditional proxy variable approach fails. We provide conditions under which the output elasticity of the variable input -- and hence the markup -- is nonparametrically point-identified. A Neyman orthogonal moment condition ensures oracle efficiency of our GMM estimator. A Monte Carlo exercise shows a large bias for the traditional proxy variable approach that decreases rapidly and nearly vanishes for our generalized control function approach.
We generalize the control function approach to production function estimation. Our generalization accommodates scenarios in which productivity evolves jointly with other unobservable factors such as latent demand shocks and the invertibility assumption underpinning the traditional proxy variable approach fails. We provide conditions under which the output elasticity of the variable input -- and hence the markup -- is nonparametrically point-identified. A Neyman orthogonal moment condition ensures oracle efficiency of our GMM estimator. A Monte Carlo exercise shows a large bias for the traditional proxy variable approach that decreases rapidly and nearly vanishes for our generalized control function approach.
AI for Pricing
Abstract
Over the last few months, AI models including large language models have improved greatly. There are now several documented examples where they have helped professional mathematical scientists prove new results, sometimes even helping resolve known open problems. In this short note, we add another example to the list, by documenting how we were able to solve a previously unsolved research problem in robust mathematical statistics with crucial help from GPT-5. Our problem concerns robust density estimation, where the observations are perturbed by Wasserstein-bounded contaminations.In a previous preprint (Chao and Dobriban, 2023, arxiv:2308.01853v2), we have obtained upper and lower bounds on the minimax optimal estimation error; which were, however, not sharp. Starting in October 2025, making significant use of GPT-5 Pro, we were able to derive the minimax optimal error rate (reported in version 3 of the above arxiv preprint). GPT-5 provided crucial help along the way, including by suggesting calculations that we did not think of, and techniques that were not familiar to us, such as the dynamic Benamou-Brenier formulation, for key steps in the analysis. Working with GPT-5 took a few weeks of effort, and we estimate that it could have taken several months to get the same results otherwise. At the same time, there are still areas where working with GPT-5 was challenging: it sometimes provided incorrect references, and glossed over details that sometimes took days of work to fill in. We outline our workflow and steps taken to mitigate issues. Overall, our work can serve as additional documentation for a new age of human-AI collaborative work in mathematical science.
Over the last few months, AI models including large language models have improved greatly. There are now several documented examples where they have helped professional mathematical scientists prove new results, sometimes even helping resolve known open problems. In this short note, we add another example to the list, by documenting how we were able to solve a previously unsolved research problem in robust mathematical statistics with crucial help from GPT-5. Our problem concerns robust density estimation, where the observations are perturbed by Wasserstein-bounded contaminations.In a previous preprint (Chao and Dobriban, 2023, arxiv:2308.01853v2), we have obtained upper and lower bounds on the minimax optimal estimation error; which were, however, not sharp. Starting in October 2025, making significant use of GPT-5 Pro, we were able to derive the minimax optimal error rate (reported in version 3 of the above arxiv preprint). GPT-5 provided crucial help along the way, including by suggesting calculations that we did not think of, and techniques that were not familiar to us, such as the dynamic Benamou-Brenier formulation, for key steps in the analysis. Working with GPT-5 took a few weeks of effort, and we estimate that it could have taken several months to get the same results otherwise. At the same time, there are still areas where working with GPT-5 was challenging: it sometimes provided incorrect references, and glossed over details that sometimes took days of work to fill in. We outline our workflow and steps taken to mitigate issues. Overall, our work can serve as additional documentation for a new age of human-AI collaborative work in mathematical science.
AI Summary
- The authors highlight the potential benefits of using AI in research, including increased productivity and well-being for mathematicians. [3]
- They also note that AI can excel at routine but lengthy calculations, freeing up time for more creative work. [3]
- They also note that human-AI collaboration can lead to new insights and solutions. [3]
- LLM: Large Language Model AI: Artificial Intelligence The use of AI in research has the potential to increase productivity and well-being for mathematicians. [3]
- Human-AI collaboration can lead to new insights and solutions. [3]
- The paper discusses the use of a large language model (LLM) in solving a research problem in mathematical statistics. [2]
Abstract
AI/ML model cards can contain a benchmarked evaluation of an AI/ML model against intended use but a one time assessment during model training does not get at how and where a model is actually used over its lifetime. Through Patra Model Cards embedded in the ICICLE AI Institute software ecosystem we study model cards as dynamic objects. The study reported here assesses the benefits and tradeoffs of adopting the Model Context Protocol (MCP) as an interface to the Patra Model Card server. Quantitative assessment shows the overhead of MCP as compared to a REST interface. The core question however is of active sessions enabled by MCP; this is a qualitative question of fit and use in the context of dynamic model cards that we address as well.
AI/ML model cards can contain a benchmarked evaluation of an AI/ML model against intended use but a one time assessment during model training does not get at how and where a model is actually used over its lifetime. Through Patra Model Cards embedded in the ICICLE AI Institute software ecosystem we study model cards as dynamic objects. The study reported here assesses the benefits and tradeoffs of adopting the Model Context Protocol (MCP) as an interface to the Patra Model Card server. Quantitative assessment shows the overhead of MCP as compared to a REST interface. The core question however is of active sessions enabled by MCP; this is a qualitative question of fit and use in the context of dynamic model cards that we address as well.
AI Summary
- The article discusses the Model Context Protocol (MCP) and its performance evaluation in serving model cards, comparing it to REST protocol. [2]
- FAIR Signposting Profile: Implementation guidelines for exposing machine-actionable navigation links using standardized HTTP headers and HTML link elements. [1]
AI for Supply Chain
Abstract
In an era marked by rapid technological advancements and complex global challenges, responsible foresight has emerged as an essential framework for policymakers aiming to navigate future uncertainties and shape the future. Responsible foresight entails the ethical anticipation of emerging opportunities and risks, with a focus on fostering proactive, sustainable, and accountable future design. This paper coins the term "responsible computational foresight", examining the role of human-centric artificial intelligence and computational modeling in advancing responsible foresight, establishing a set of foundational principles for this new field and presenting a suite of AI-driven foresight tools currently shaping it. AI, particularly in conjunction with simulations and scenario analysis, enhances policymakers' ability to address uncertainty, evaluate risks, and devise strategies geared toward sustainable, resilient futures. However, responsible foresight extends beyond mere technical forecasting; it demands a nuanced understanding of the interdependencies within social, environmental, economic and political systems, alongside a commitment to ethical, long-term decision-making that supports human intelligence. We argue that AI will play a role as a supportive tool in responsible, human-centered foresight, complementing rather than substituting policymaker judgment to enable the proactive shaping of resilient and ethically sound futures. This paper advocates for the thoughtful integration of AI into foresight practices to empower policymakers and communities as they confront the grand challenges of the 21st century.
In an era marked by rapid technological advancements and complex global challenges, responsible foresight has emerged as an essential framework for policymakers aiming to navigate future uncertainties and shape the future. Responsible foresight entails the ethical anticipation of emerging opportunities and risks, with a focus on fostering proactive, sustainable, and accountable future design. This paper coins the term "responsible computational foresight", examining the role of human-centric artificial intelligence and computational modeling in advancing responsible foresight, establishing a set of foundational principles for this new field and presenting a suite of AI-driven foresight tools currently shaping it. AI, particularly in conjunction with simulations and scenario analysis, enhances policymakers' ability to address uncertainty, evaluate risks, and devise strategies geared toward sustainable, resilient futures. However, responsible foresight extends beyond mere technical forecasting; it demands a nuanced understanding of the interdependencies within social, environmental, economic and political systems, alongside a commitment to ethical, long-term decision-making that supports human intelligence. We argue that AI will play a role as a supportive tool in responsible, human-centered foresight, complementing rather than substituting policymaker judgment to enable the proactive shaping of resilient and ethically sound futures. This paper advocates for the thoughtful integration of AI into foresight practices to empower policymakers and communities as they confront the grand challenges of the 21st century.
AI Summary
- AI's ability to analyze extensive datasets, model complex systems, and simulate alternative futures presents a promising pathway to support responsible foresight. [3]
- The ultimate goal of responsible computational foresight is to create a partnership where AI's computational power complements human judgment and ethical insight. [3]
- Responsible computational foresight is about supporting humans in understanding and designing the future. [2]
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