Why we think this paper is great for you:
This paper directly addresses price predictions and economic impact, which is highly relevant to your interest in optimizing pricing strategies. It explores how predictive methods can be applied to manage energy arbitrage effectively.

Abstract
This study examines the economic impact of post-hoc uncertainty discounting in predictive energy management, specifically in battery energy arbitrage. A 2.2 MWh, 1.1 MW Tesla battery, emulating operations at the University of Queensland's St. Lucia campus, is used as a test system. Traditionally, Model Predictive Control (MPC) frameworks rely on deterministic spot price forecasts from the Australian Energy Market Operator (AEMO) to optimize battery scheduling. However, these forecasts lack uncertainty awareness, making arbitrage strategies vulnerable to extreme price volatility. To address this, we propose simple heuristic uncertainty discounting methods, which require no access to the predictive model's architecture or inputs. By integrating these strategies into existing MPC frameworks, we demonstrate a more than 20% improvement in economic returns under identical operational constraints. This approach enhances decision-making in energy arbitrage while remaining practical, scalable, and independent of specific forecasting models

AI Summary

• Black Box Predictive Model: A forecasting model whose internal architecture, parameters, or input data are inaccessible to the decision-maker, who only receives deterministic outputs. [3]
• Post-hoc Uncertainty Discounting: Applying uncertainty considerations to a deterministic forecast after it has been generated, without modifying the forecasting model itself. [3]
• Implementing simple heuristic uncertainty discounting in Model Predictive Control (MPC) for battery energy arbitrage can increase economic returns by over 20% without requiring access to the underlying black-box forecasting model. [2]
• Prioritizing near-term forecasts by exponentially discounting the influence of longer lead-time predictions (e.g., using a Power Law with a gamma_0 of 0.95) significantly enhances arbitrage profitability. [2]
• The proposed uncertainty discounting methods are 'plug-and-play' heuristics, enabling decision-makers to integrate uncertainty awareness into existing MPC frameworks using third-party deterministic forecasts with O(1) computational complexity. [2]
• L1 regularization (s=1) in the uncertainty-aware MPC objective function generally yields slightly better profit margins compared to L2 regularization, suggesting a benefit from inducing sparsity in optimal power schedules. [2]
• Deterministic price forecasts from market operators like AEMO, despite their sophistication, exhibit staggering percentage errors, making them unreliable for energy arbitrage without explicit uncertainty consideration. [2]
• Uncertainty Discounting: A heuristic strategy that assigns progressively decreasing weights to price forecasts as their lead time increases, reflecting higher inherent uncertainty in farther-future predictions. [2]
• Deterministic Price Forecasts: Single-point predictions of future energy prices without accompanying confidence intervals or probabilistic uncertainty metrics. [2]
• Model Predictive Control (MPC) for Battery Arbitrage: An optimization framework that uses future price forecasts to determine optimal battery charging and discharging schedules to maximize economic benefits, typically by buying low and selling high. [2]

Why we think this paper is great for you:
This paper focuses on online inventory balancing and resource allocation, which aligns perfectly with your interest in optimizing supply chain operations. It provides insights into managing product consumption under uncertainty.

Abstract
In classic adversarial online resource allocation problems such as AdWords, customers arrive online while products are given offline with a fixed initial inventory. To ensure revenue guarantees under uncertainty, the decision maker must balance consumption across products. Based on this, the prevalent policy "inventory balancing (IB)" has proved to be optimal or near-optimal competitive in almost all classic settings. However, these models do not capture various forms of inventory shocks on the supply side, which play an important role in real-world online assortment and can significantly impact the revenue performance of the IB algorithm. Motivated by this paradigm, we introduce a variant of online assortment planning with inventory shocks. Our model considers adversarial exogenous shocks (where supply increases unpredictably) and allocation-coupled endogenous shocks (where an inventory reduction is triggered by the algorithms and re-adjusted after a usage duration), whose combination leads to non-monotonic inventory fluctuations. As our main result, we show the robustness of IB-type strategies against such shocks by designing a new family of optimal competitive algorithms called "Batched Inventory Balancing (BIB)." Using a novel randomized primal-dual method, we bound the competitive ratio of BIB against optimal offline. We show that with proper choice of a certain parameter, this competitive ratio is asymptotically optimal and converges to (1-1/e) as initial inventories grow, in contrast to the original IB which no longer achieves the optimal ratio in this new model. Moreover, we characterize BIB's competitive ratio parametric by its penalty function and show that it matches exactly the competitive ratio of IB without shocks. Our refined analysis reduces the dual construction to a combinatorial "interval assignment problem" whose algorithmic solution may be of independent interest.

Why we think this paper is great for you:
This paper explores the supply chain aspects of AI itself, offering a unique perspective on managing risks and trustworthiness within AI systems. This is particularly relevant given your interest in the intersection of AI and supply chain.

Abstract
Risks associated with the use of AI, ranging from algorithmic bias to model hallucinations, have received much attention and extensive research across the AI community, from researchers to end-users. However, a gap exists in the systematic assessment of supply chain risks associated with the complex web of data sources, pre-trained models, agents, services, and other systems that contribute to the output of modern AI systems. This gap is particularly problematic when AI systems are used in critical applications, such as the food supply, healthcare, utilities, law, insurance, and transport. We survey the current state of AI risk assessment and management, with a focus on the supply chain of AI and risks relating to the behavior and outputs of the AI system. We then present a proposed taxonomy specifically for categorizing AI supply chain entities. This taxonomy helps stakeholders, especially those without extensive AI expertise, to "consider the right questions" and systematically inventory dependencies across their organization's AI systems. Our contribution bridges a gap between the current state of AI governance and the urgent need for actionable risk assessment and management of AI use in critical applications.

Why we think this paper is great for you:
This paper delves into automated market making for goods, which directly relates to dynamic pricing mechanisms and managing supply and demand. You'll find its insights into capacity and utility highly pertinent.

Abstract
We study decentralized markets for goods whose utility perishes in time, with compute as a primary motivation. Recent advances in reproducible and verifiable execution allow jobs to pause, verify, and resume across heterogeneous hardware, which allow us to treat compute as time indexed capacity rather than bespoke bundles. We design an automated market maker (AMM) that posts an hourly price as a concave function of load--the ratio of current demand to a "floor supply" (providers willing to work at a preset floor). This mechanism decouples price discovery from allocation and yields transparent, low latency trading. We establish existence and uniqueness of equilibrium quotes and give conditions under which the equilibrium is admissible (i.e. active supply weakly exceeds demand). To align incentives, we pair a premium sharing pool (base cost plus a pro rata share of contemporaneous surplus) with a Cheapest Feasible Matching (CFM) rule; under mild assumptions, providers optimally stake early and fully while truthfully report costs. Despite being simple and computationally efficient, we show that CFM attains bounded worst case regret relative to an optimal benchmark.

Why we think this paper is great for you:
This paper discusses the application of industrial AI for enhancing decision support, a core component of optimizing complex processes. It offers valuable perspectives on leveraging AI for strategic decision-making.

Abstract
The construction industry is presently going through a transformation led by adopting digital technologies that leverage Artificial Intelligence (AI). These industrial AI solutions assist in various phases of the construction process, including planning, design, production and management. In particular, the production phase offers unique potential for the integration of such AI-based solutions. These AI-based solutions assist site managers, project engineers, coordinators and other key roles in making final decisions. To facilitate the decision-making process in the production phase of construction through a human-centric AI-based solution, it is important to understand the needs and challenges faced by the end users who interact with these AI-based solutions to enhance the effectiveness and usability of these systems. Without this understanding, the potential usage of these AI-based solutions may be limited. Hence, the purpose of this research study is to explore, identify and describe the key factors crucial for developing AI solutions in the construction industry. This study further identifies the correlation between these key factors. This was done by developing a demonstrator and collecting quantifiable feedback through a questionnaire targeting the end users, such as site managers and construction professionals. This research study will offer insights into developing and improving these industrial AI solutions, focusing on Human-System Interaction aspects to enhance decision support, usability, and overall AI solution adoption.

Why we think this paper is great for you:
This paper investigates profit optimization within a business context, offering a framework for maximizing returns under various constraints. Its focus on optimization principles will be valuable to you.

Abstract
We develop a formalism for insurance profit optimisation for the in-force business constraint by regulatory and risk policy related requirements. This approach is applicable to Life, P&C and Reinsurance businesses and applies in all regulatory frameworks with a solvency requirement defined in the form of a solvency ratio, notably Solvency II and the Swiss Solvency Test. We identify the optimal asset allocation for profit maximisation within a pre-defined risk appetite and deduce the annual opportunity cost faced by the insurance company.

Why we think this paper is great for you:
This paper explores adaptive sampling techniques in machine learning, which can be foundational for developing more efficient data collection strategies for predictive models. It touches upon advanced learning concepts that might interest you.

Abstract
We study the tradeoff between sample complexity and round complexity in on-demand sampling, where the learning algorithm adaptively samples from $k$ distributions over a limited number of rounds. In the realizable setting of Multi-Distribution Learning (MDL), we show that the optimal sample complexity of an $r$-round algorithm scales approximately as $dk^{Θ(1/r)} / ε$. For the general agnostic case, we present an algorithm that achieves near-optimal sample complexity of $\widetilde O((d + k) / ε^2)$ within $\widetilde O(\sqrt{k})$ rounds. Of independent interest, we introduce a new framework, Optimization via On-Demand Sampling (OODS), which abstracts the sample-adaptivity tradeoff and captures most existing MDL algorithms. We establish nearly tight bounds on the round complexity in the OODS setting. The upper bounds directly yield the $\widetilde O(\sqrt{k})$-round algorithm for agnostic MDL, while the lower bounds imply that achieving sub-polynomial round complexity would require fundamentally new techniques that bypass the inherent hardness of OODS.