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on Computational Economics |
| By: | Cosmin Borsa; Michael Ludkovski |
| Abstract: | Simulation based solvers for optimal stopping problems must discretize the stopping decision. Under classical dynamic programming, a coarse exercise grid with only a few stopping opportunities can materially undervalue the optimal expected reward, whereas on a very fine grid, approximation errors accumulate through the backward recursion. To remove this limitation, we develop a new reinforcement-learning inspired algorithm that enables us to learn the exercise rule at arbitrarily fine time resolution. Our CARLOS (Continuous-time Adaptive Reinforcement Learning for Optimal Stopping) algorithm utilizes an aggregate deep neural network (ADNN) to learn a joint space-time decision boundary. Starting from a coarse time grid, we progressively increase the frequency of stopping opportunities, while in parallel training the ADNN to refine its timing-value estimates. We moreover design an adaptive sampling strategy that gradually concentrates training effort near the stopping boundary. Benchmarked results show that CARLOS delivers higher prices than existing Bermudan solvers, approaching the American upper bound, and achieves high computational efficiency relative to non-RL comparators. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.17545 |
| By: | Alam, M. Jahangir; Boyle, Shane; Li, Huiyu; Sekhposyan, Tatevik |
| Abstract: | Recent research suggests that generic large language models (LLMs) can match the accuracy of traditional methods when forecasting macroeconomic variables in pseudo out-of-sample settings generated via prompts. This paper assesses the out-of-sample forecasting accuracy of LLMs by eliciting real-time forecasts of U.S. inflation from ChatGPT. We find that out-of-sample predictions are largely inaccurate and stale, even though forecasts generated in pseudo out-of-sample environments are comparable to existing benchmarks. Our results underscore the importance of out-of-sample benchmarking for LLM predictions. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21057 |
| By: | Manon Reusens; Sofie Goethals; David Martens |
| Abstract: | Large language models (LLMs) are increasingly deployed as autonomous agents that make consumption decisions on behalf of users. This shift raises fundamental questions for consumer theory, which has traditionally modeled humans as the primary decision-makers. In this paper, we introduce LLM Consumer Behavior Theory, a new field of study concerned with analyzing consumer behavior in agentic markets. Drawing on classical and behavioral economics alongside recent advances in Natural Language Processing, we formalize how human preferences are reflected and acted upon by LLM-based agents, and how agent-level decisions aggregate into market demand. We unify previously fragmented literature on LLM decision-making, human behavior simulation, and preference elicitation under a common economic lens, highlighting where assumptions, such as rationality and heterogeneity, may fail in agentic markets. Rather than providing empirical validation, this paper outlines the scope of LLM consumer behavior and identifies open research questions related to alignment, preference representation, and market dynamics. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.18005 |
| By: | Masood Tadi; Milan Fičura; Jiří Witzany |
| Abstract: | We study natural gas storage valuation under a stochastic futures term structure using deep reinforcement learning (DRL). The storage problem is formulated as a continuous-state, continuous-action Markov Decision Process and solved using the Deep Deterministic Policy Gradient (DDPG) algorithm with Prioritized Experience Replay (PER) buffer and a constraint-aware policy network. We benchmark the approach against intrinsic and rolling intrinsic strategies and find that DRL consistently outperforms intrinsic valuation and achieves competitive performance relative to rolling intrinsic in markets with jumps and seasonality. The results show that DRL provides a practical valuation framework that captures additional extrinsic value under realistic market dynamics and operational constraints. |
| Keywords: | Natural Gas Storage, Rolling Intrinsic Valuation, Deep Reinforcement Learning |
| Date: | 2026–06–12 |
| URL: | https://d.repec.org/n?u=RePEc:prg:jnlwps:v:6:y:2026:id:6.003 |
| By: | Andreas Ferrara |
| Abstract: | Large language models (LLMs) are lowering the entry barriers to working with exciting data sources that used to require strong data science skills, such as handwritten ledgers, text, images, or sound recordings. This guide provides an introduction for researchers who are new to LLMs. It sets out a step-by-step workflow for turning a research idea into working code and data, and describes the four main ways of interacting with an LLM: the chat window, editor-integrated assistants, agentic coding tools, and the API. It then works through the decisions a practitioner meets in sequence, beginning with whether an LLM is the right tool and whether the data are allowed to be sent to one, then how to select models, write prompts, manage context limits, and control costs, and finally how to validate, reproduce, document, and correct LLM-generated measures in regression settings. A review of recent research shows how these tools already extract, link, harmonize, and classify historical data at scale. Four worked examples with replication files illustrate the use of LLMs. They classify emotions in paintings, link census records without names, measure newspaper salience and sentiment around the 1882 Chinese Exclusion Act, and score the emotional delivery of Franklin D. Roosevelt's wartime speeches. The guide also condenses the workflow, the best-practice recommendations, and the preparation of replication packages into summary tables and checklists to aid applied economists. |
| JEL: | C55 C8 N0 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35374 |
| By: | Dirk Bergemann (Yale University); Soheil Ghili (Yale University); Xinyang Hu (Yale University); Chuanhao Li (Yale University); Zhuoran Yang (Yale University) |
| Abstract: | Bilateral bargaining under incomplete information provides a controlled testbed for evaluating large language model (LLM) agent capabilities. Bilateral trade demands individual rationality, strategic surplus maximization, and cooperation to realize gains from trade. We develop a structured bargaining environment in which LLMs negotiate via tool calls within an event-driven simulator, separating binding offers from natural-language messages to enable automated evaluation. The environment serves two purposes: as a benchmark for frontier models and as a training environment for open-weight models via reinforcement learning. In benchmark experiments, a round-robin tournament among five frontier models (15, 000 negotiations) reveals that effective strategies implement price discrimination through sequential offers. Aggressive anchoring, calibrated concession, and temporal patience are associated with both the highest surplus share and the highest deal rate. Accommodating strategies that concede quickly disable price discrimination in the buyer role, yielding the lowest surplus capture and deal completion. Strategically competent models scale their behavior proportionally to item value, maintaining consistent performance across price tiers; weaker models perform well only when wide zones of possible agreement compensate for suboptimal strategies. In training experiments, we fine-tune Qwen3 (8B, 14B) via supervised fine-tuning (SFT) followed by Group Relative Policy Optimization (GRPO) against a fixed frontier opponent. The two stages optimize competing objectives: SFT approximately doubles surplus share but reduces deal rates, while RL recovers deal rates but erodes surplus gainsÑa tension traceable to the reward structure. SFT also compresses surplus variation across price tiers, and this compression generalizes to opponents unseen during training, suggesting that behavioral cloning instills proportional strategies rather than memorized price points. |
| Date: | 2026–04–01 |
| URL: | https://d.repec.org/n?u=RePEc:cwl:cwldpp:2514 |
| By: | Yang, Yucheng; Wang, Chiyuan; Schaab, Andreas; Moll, Benjamin |
| Abstract: | We present a new approach to formulating and solving heterogeneous agent models with aggregate risk. We replace the cross-sectional distribution with low-dimensional prices as state variables and let agents learn equilibrium price dynamics directly from simulated paths. To do so, we introduce a "structural reinforcement learning" (SRL) method which treats prices via simulation while exploiting agents’ structural knowledge of their own individual dynamics. Our SRL method yields a general and highly efficient global solution method for heterogeneous agent models that sidesteps the Master equation and handles models traditional methods struggle with, like those with nontrivial market-clearing conditions. We illustrate the approach in the Krusell-Smith model, the Huggett model with aggregate shocks, and a HANK model with a forward-looking Phillips curve, all of which we solve globally within minutes. |
| Keywords: | Reinforcement learning |
| JEL: | E00 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20980 |
| By: | Ziwen Zu |
| Abstract: | Large language models (LLMs), a prominent form of artificial intelligence (AI), are becoming everyday interfaces for political questions, but most exchanges are dyadic rather than audiencefacing. This paper asks whether AI conversation functions as a new arena for political expression or as a conversational intermediary for routine political demand. Using 4.30 million humanAI conversations from three large public datasets, we apply two validated classifiers to user messages, identifying political content, use case, and expressed ideology. Political content appears in 3.9% of conversations, varies sharply by platform publicness and conversation depth, and is mostly practical: users ask for information, draft text, and process documents far more often than they state opinions. A regression-discontinuity-in-time design around the 2024 U.S. presidential result call shows that the call changed the expressive subset: among U.S. users, stance-taking, affective language, and ideological extremity rose; comparable conversations elsewhere did not. AI conversation is less a public square than a conversational political intermediary, absorbing routine demand and becoming expressive when major events make political stakes explicit. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.00551 |
| By: | Gu, Gyun Cheol |
| Abstract: | We replicate and extend the ultimatum game experiment of Araujo and Uhlig (2026) to five consumer-facing large language models (LLMs)—ChatGPT, Claude, Copilot, Gemini Flash, and Gemini Pro—across four scenario types (HH, HA, AH, AA), four stake levels ($10 to $10, 000), and ten repetitions per configuration, yielding 6, 832 proposer and 8, 532 responder observations. Four findings emerge. First, all five models propose shares in the 30–47% range, squarely within the human empirical benchmark and absent the extreme behavioral modes documented in earlier research-grade models, suggesting that alignment training has compressed the behavioral distribution toward human norms. Second, every model exhibits twosided sensitivity to human presence: proposed shares rise when the Responder is human (+4 to +25 p.p.) and minimum acceptable thresholds rise when acting on behalf of a human (+11 to +26 p.p.), a pattern that survives even when no human principal is being served and is inconsistent with simple principal–agent alignment. Third, all five models forgo 25–63% of feasible payoff, confirming that consumer LLMs are not payoff-maximizing agents. Fourth, responder thresholds decline significantly with stake size across all models— consistent with rational expected-utility behavior—while proposer stake sensitivity is heterogeneous. We interpret these patterns as evidence of identity internalization: successive rounds of reinforcement learning with human feedback cause models to behave as if they are human rather than merely as if they prefer human-like outcomes. |
| Keywords: | ultimatum game, large language models, human identity, alignment, RLHF, behavioral economics |
| JEL: | C70 C90 |
| Date: | 2026–06–07 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:129505 |
| By: | Isidro Moroso Varona; Jakub Micha\'nk\'ow; Pawe{\l} Sakowski |
| Abstract: | This paper studies the use of randomized neural networks for the estimation of exposure profiles and unilateral CVA of American options within a Monte Carlo framework. The analysis is carried out separately under both Black-Scholes and Heston dynamics, combining American option valuation, expected exposure and potential future exposure estimation, and unilateral CVA calculation with portfolio netting effects. The numerical experiment compares this approach with the classical Least-Squares Monte Carlo (LSM) used as a benchmark in both low-dimensional single-asset and high-dimensional multi-asset scenarios, and also includes a path convergence test and a sensitivity analysis. The results show that the randomized feedforward neural network approach preserves convergence to the LSM benchmark when it is extended from pricing to exposure and CVA estimation, while its main advantage appears in high-dimensional problems, where it scales more efficiently and leads to lower computational cost. These results support the use of randomized neural networks as a useful alternative for exposure and CVA estimation in high-dimensional American-style options. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.24309 |
| By: | Shakya Jayakody; Prarthinie Jayakody |
| Abstract: | Simulating financial markets at scale with multi-agent (Agent-Based) models is critical for market design, regulatory stress-testing, and reinforcement learning, but traditional CPU simulators are bottlenecked by sequential processing while vectorized GPU frameworks suffer from kernel-launch overhead and redundant global-memory round-trips. We formalize, analyze, and evaluate a reusable parallel design pattern: persistent, state-carrying clearing for iterative multi-agent reductions. By caching mutable simulation state in thread-block shared memory across step boundaries, aggregating agent actions via shared-memory atomics, and resolving the clearing function cooperatively, the pattern reduces the per-step critical-path depth from Theta(L+A) for sequential clearing (L price-grid ticks, A agents) to Theta(log L + ceil(A/L)) and makes global-memory traffic independent of the step count. We implement this in KineticSim, a lightweight GPU execution engine that simulates massive ensembles of limit-order books in parallel, reaching a peak throughput of over 54.7 billion agent-events per second. On a fixed workload it delivers speedups of 3406x over CPU (NumPy), 27.8x over PyTorch GPU, 42.8x over JAX GPU, and 8.4x over a naive custom CUDA baseline, while using roughly an order of magnitude less GPU memory than PyTorch. Across 53 configurations the two custom CUDA engines produce bitwise-identical order books, and aggregate statistics match the CPU reference to within 0.1%. The pattern generalizes to other iterative multi-agent workloads requiring state-persistent, block-localized reductions. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.21784 |
| By: | Hannes Wallimann; C\'edric Br\"utsch; Martin Huber |
| Abstract: | Fare evasion generates substantial revenue losses for public transport operators and is typically combated through fare inspections, yet little is known about how the mode of inspection-uniformed versus plainclothes-affects detection efficiency. Using a unique dataset of 21, 727 inspection records from PostAuto, the largest regional bus operator in Switzerland, we apply causal machine learning to estimate the causal effect of inspector visibility on inspection efficiency, defined as detected fare evaders per inspection hour. Our results indicate that plainclothes inspections are, on average, significantly more effective than uniformed inspections, with an estimated average treatment effect of -0.173 incidents per hour, corresponding to a relative reduction of approximately 26%. Heterogeneity analyses find no evidence of systematic effect variation across contextual characteristics, suggesting that the superiority of plainclothes inspections is robust and pervasive across the PostAuto network. When applying optimal policy learning (based on policy trees) to optimally target subgroups by one or the other treatment depending on relative effectiveness, plainclothes inspections are recommended for the large majority of contexts (83.3%), with uniformed inspections suggested only for lines characterised by a below-median share of foreign residents and above-median population size. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.24181 |
| By: | Shujie Li (Paderborn University); Yuanhua Feng (Paderborn University) |
| Abstract: | Macroeconomic time series forecasting is crucial for guiding government policy decisions, business strategies, and understanding economic trends. However, predicting macroeconomic variables remains a significant challenge. The complexity of economic systems, insufficient data, high levels of volatility complicate the task of accurate forecasting. To enhance forecasting accuracy, we propose two novel models to capture both linear and nonlinear dynamics. First, we generalize the random walk model by incorporating a drift term, which is estimated using a simple neural network model. Second, a hybrid model is introduced to combine local linear regression and the neural network model. Additionally, we adopt other models from Fritz et al. (2024) for combination. These models are combined using a simple averaging method. Our results demonstrate that the newly proposed neural network-based models produce the lowest average MASE. Additionally, model combination is an effective strategy for enhancing the performance of GDP forecasting in most countries and it is less risky than relying on a single model. |
| Keywords: | nonparametric approaches, combination of forecasting, NNAR, Random Walk |
| JEL: | C14 C51 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:pdn:ciepap:172 |
| By: | Tianjia Dong; Nadav Kunievsky; James A. Evans |
| Abstract: | Large language models are increasingly deployed as autonomous decision makers, yet the behavioral mapping they exhibit can vary substantially across decision environments that are payoff-equivalent by construction-environments that share identical payoff-relevant structure but differ in surface presentation. This sensitivity renders suite-based evaluation fragile and raises a fundamental question of behavioral portability: how well does a behavioral mapping learned in one decision environment informative on another that preserves the same underlying incentive structure? We introduce a formal framework to measure this property. Our protocol fits an interpretable behavioral model on data pooled from a set of source environments and evaluates its out-of-sample predictive performance in a held-out target environment, benchmarking against an oracle trained directly on target data. Portability is quantified via a loss-agnostic measure that delivers worst-case bounds on the performance of the induced prediction-action mapping in the target environment. In controlled experiments spanning seven canonical economic decision problems, we document substantial and systematic portability losses, suggesting that behavioral characterizations of LLMs obtained in one decision environment cannot be assumed to transfer reliably to structurally equivalent alternatives. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.22797 |
| By: | Christopher W. Karvetski; Sheldon S. Huang; Simas Ku\v{c}inskas; Nadja Flechner; Jingyu Hu; Philip Tetlock; Ezra Karger |
| Abstract: | Decision-makers routinely rely on expert judgments accompanied by written explanations, yet explanation quality is difficult to measure at scale. Forecasting tournaments offer a natural testing ground: probabilistic judgments are paired with natural-language rationales and scored against realized outcomes. We introduce Explanation Quality Markers (EQMs), a set of sixty theory-guided reasoning patterns scored by large language models (LLMs). In a pre-registered analysis of over 55, 000 forecast-rationale pairs from a multiyear forecasting tournament, EQMs predict accuracy at both the forecast and forecaster levels, consistently outperforming pre-LLM text-analysis methods. More than 90% of statistically significant pattern-level EQM-accuracy correlations match our directional hypotheses. The signal is asymmetric: EQMs identify likely underperformers more reliably than they distinguish the very best forecasters. Benchmarked against traditional indicators of forecasting skill, EQMs are the strongest predictor at the forecast level and competitive at the forecaster level, though weaker than prior accuracy. Human ratings of rationale quality are less consistently correlated with accuracy and place disproportionate weight on rationale length. Results transfer to an independent forecasting study. EQMs provide a scalable, interpretable method for extracting judgment-relevant information from written explanations. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.30987 |
| By: | Sergio A. Correia; Stephan Luck; Emil Verner |
| Abstract: | Banking crises are commonly associated with bank runs and banking panics, yet our empirical understanding of bank runs is constrained by a lack of bank-level data. In a new paper, we use large language models (LLMs) to extract information on bank runs from millions of digitized historical newspaper pages, creating the most comprehensive database of bank runs in U.S. history. Every bank run episode that we identify is documented on a companion website where users can browse and examine individual episodes, and read the original newspaper articles. In this post, we describe how we built this dataset and discuss what its basic features reveal. |
| Keywords: | bank runs; banking crises; bank failures; deposit insurance; liquidity; solvency; artificial intelligence (AI) |
| JEL: | G01 |
| Date: | 2026–07–07 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:103501 |
| By: | Kwon, Byeungchun; Park, Taejin; Rungcharoenkitkul, Phurichai; Smets, Frank |
| Abstract: | Macroeconomic indicators provide quantitative signals that must be pieced together and interpreted by economists. We propose a reversed approach of parsing press narratives directly using Large Language Models (LLM) to recover growth and inflation sentiment indices. A key advantage of this LLM-based approach is the ability to decompose aggregate sentiment into its drivers, readily enabling an interpretation of macroeconomic dynamics. Our sentiment indices track hard-data counterparts closely, providing an accurate, near real-time picture of the macroeconomy. Their components–demand, supply, and deeper structural forces–are intuitive and consistent with prior model-based studies. Incorporating sentiment indices improves the forecasting performance of simple statistical models, pointing to information unspanned by traditional data. |
| JEL: | E30 E44 E60 C55 C82 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20828 |
| By: | Thijs van den Berg |
| Abstract: | Many models in quantitative finance have no closed-form option prices and rely on slow, noisy Monte Carlo simulation; neural surrogates restore speed but offer no error guarantees. We present a general recipe for surrogates that are fast, with bounded and verifiable error, applicable to any simulation-based density model. A Mixture Density Network maps parameters and maturity to the terminal return density as a Gaussian mixture, so prices, implied volatilities, and Greeks follow in closed form as an arbitrage-free mixture of lognormals, with a CDF-matching loss aligned to pricing error. A distribution-free Monte Carlo noise floor, $\sqrt{1/(6N)}$, quantifies the best accuracy achievable at a given simulation budget and decomposes the out-of-sample error into four controllable terms. We demonstrate the method on GJR--GARCH, where the surrogate reaches an out-of-sample CDF error of $1.4\times10^{-4}$, within $10\%$ of the noise floor, and prices each option in a few microseconds on a single CPU core, or under a microsecond on a GPU. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.15502 |
| By: | Lin Liu; Rajarshi Mukherjee; James M Robins |
| Abstract: | Structure-agnostic (SA) models introduced by Balakrishnan et al. (2026) aim to reflect the general lack of knowledge of structural assumptions on data-generating laws such as smoothness or sparsity in practice. Roughly speaking, SA models restrict the observed-data generating law to be in some rn-neighborhood of (black-box machine learning) estimates, treated as given and fixed, where rn encodes the convergence rates of the estimates to the truth. Under SA models, Balakrishnan et al. (2026) show that the popular Double Machine Learning (DML) estimators for three functionals, the quadratic functional in the Gaussian sequence model, the quadratic density integral functional and the expected conditional covariance, are minimax. However, minimax estimators may be inadmissible. In this paper, we show that, for the first two of the three functionals, the DML estimator is asymptotically inadmissible under the SA model. In particular, we show that these two functionals fall into a class of functionals, which we refer to as the monotone bias class. For this class, we exhibit second-order (U-statistic) estimators, which asymptotically dominate DML estimators, under the SA model. These second-order estimators are empirical higher-order influence function (HOIF) estimators introduced in Liu et al. (2017). Furthermore, the empirical HOIF estimator, like the DML estimator, is minimax for the third functional (the expected conditional covariance), although neither asymptotically dominates the other. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.22391 |
| By: | Emre Yusuf; Ren Takahashi; Jayabrata Bhaduri |
| Abstract: | Rare events in time series are critical to model but hard to learn due to data scarcity. Current generative models struggle with extreme values. We observe that rare events leave distinct topological fingerprints - transitions in Betti numbers from point-cloud embeddings - that are more stable and discriminative than statistical moments. We introduce PHINN, a flow-matching framework using dynamic Betti curves as conditioning signals and a persistence landscape loss for homology consistency. It scales to multivariate data, includes a natural-language interface to set Betti targets, supports cross-domain meta-learning and few-shot generation, and provides certified adversarial robustness. On financial, epidemiological, and multi-modal benchmarks, PHINN outperforms statistical and diffusion baselines in topological fidelity (beta-RMSE down 41-63%, transition accuracy up 84%) and matches jump-diffusion models in tail coverage while exceeding them in shape fidelity. All results have 95% confidence intervals. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.15452 |
| By: | Sebastian Jensen; Siem Jan Koopman |
| Abstract: | We propose a new treatment of nonlinear regression with serially correlated disturbances that incorporates autoregressive moving average structures into feedforward neural networks. The resulting model provides an alternative to modeling temporal dependence using lagged variables. In simulations, the proposed method accurately recovers regression functions of varying complexity and the underlying error dynamics across a range of time-series lengths and signal-to-noise ratios. Finite-sample properties and out-of-sample predictive performances are shown to be robust to model misspecification induced by omitted lagged variables and incorrect specification of the error dynamics. Cloud cover is an important factor in climate projections. In an empirical study of cloud cover prediction for a grid of locations within and around the Mediterranean Sea, our proposed model yields more accurate predictions than existing methods, including long short-term memory networks. Improvements are observed broadly and are particularly pronounced in mountain areas relative to linear models with serially correlated errors, consistent with the presence of stronger nonlinear effects in cloud composure in such regions. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.22483 |
| By: | Sebastien Lleo; Wolfgang Runggaldier |
| Abstract: | This paper develops a reinforcement-learning approach to continuous-time risk-sensitive benchmarked asset allocation in a partly model-based setting. The benchmarked problem does not directly fit the standard Markovian stochastic-control template: the state is uncontrolled, whereas the terminal reward contains a controlled It\^o integral. We use free energy-entropy duality to reformulate the problem as a linear-quadratic-Gaussian stochastic differential game under an equivalent probability measure, yielding explicit finite- and infinite-horizon saddle-point solutions. This structure guides a continuous-time $q$-learning actor-critic method: the quadratic value function motivates the critic, while the affine saddle-point controls motivate deterministic actors for the portfolio allocation and adversarial control. The learned allocation admits an economic interpretation through fractional Kelly decompositions. A proof-of-concept implementation calibrated to U.S. equity data shows that the actors learn the optimal policy with high accuracy and reveals a favorable asymmetry: the portfolio actor receives a cleaner learning signal than the auxiliary adversarial actor. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.20903 |
| By: | Daniele Maria Di Nosse; Fabrizio Lillo |
| Abstract: | Automated Market Makers based on concentrated liquidity, such as Uniswap v3, significantly improve capital efficiency but expose Liquidity Providers (LPs) to adverse selection costs, formalized as Loss-Versus-Rebalancing (LVR). While theoretical literature quantifies these costs, the interplay between realistic blockchain microstructure and endogenous pricing mechanisms remains under-explored. This paper develops a granular Agent-Based Model of a Uniswap v3 pool interacting with a stochastic reference market governed by Heston volatility dynamics. The framework incorporates discrete block propagation, mempool latency, and a heterogeneous population of agents, including latency-sensitive arbitrageurs, smart routers, Maximal Extractable Value searchers, and active LPs benchmarked against a frictionless rebalancing strategy. We propose and evaluate dynamic fee schedules driven by volatility and order-flow toxicity proxies intended to compensate LPs for adverse-selection losses. Our simulations investigate the conditions under which LPs can achieve positive hedged Profit and Loss (fees minus LVR). The analysis suggests that dynamic fee adjustments can improve hedged LP profitability mainly by increasing fee income in states associated with stale-price risk. Depending on the configuration, these rules may also affect realized LVR, but the current aggregate results support compensation for LVR more directly than a reduction of LVR itself. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.23070 |
| By: | Jan H. R. Dressler; Peter Kurz; Winfried J. Steiner |
| Abstract: | Although discrete choice (choice-based conjoint) analysis has become a widely used technique for the elicitation of consumer preferences and hence a foundation for product design, to the best of our knowledge, there exists neither free and open-source nor commercial software that covers the game-theoretic simulation of competitive reactions among firms based on discrete choice models to improve decision making beyond traditional product (line) optimization. The R package cash (conjoint + Nash) does not only provide functions to fill this gap but comprises an entire simulation pipeline including the upstream processes of discrete choice analysis itself. cash ranges from preference generation, choice design, error and response simulation, through Bayesian model estimation and evaluation, to Nash equilibrium computation. Doing so, it partly draws from established R packages concerned with discrete choice analysis. While the structure of cash generally aims towards end-to-end simulation as well as simulation of competitive dynamics based on real data, all its key elements mentioned above may be of use independently of each other. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.15593 |
| By: | Quanyan Zhu |
| Abstract: | Agentic AI systems are increasingly being deployed as productive resources in organizational workflows, yet existing evaluation methods primarily measure isolated technical performance rather than economic contribution. This paper introduces \emph{Agentomics}, a workflow-based framework for valuing, attributing, and pricing human and artificial agents. The framework models a workflow as a configuration of heterogeneous agents whose collective performance determines gross value, deployment cost, reliability, and expected failure loss. Workflow value is treated as a team-level quantity that may include complementarities, substitution effects, bottlenecks, and nonlinear production; additive stage-level value is only a special case. Building on this workflow model, the paper formulates AI deployment as a coalition-formation problem and defines coalition value as the incremental net surplus generated relative to a benchmark human workflow. The Shapley value is then used to attribute economic surplus among participating AI agents, yielding a principled connection among valuation, accountability, and market pricing. The resulting Shapley pricing equilibrium provides a normative benchmark for assessing whether agent prices reflect expected marginal contribution. A security-operations case study illustrates how the framework accounts for productivity gains, deployment costs, reliability losses, and coalition-level complementarities in hybrid human--AI workflows. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.14769 |
| By: | Likai Chen; Weining Wang |
| Abstract: | Forecasting is a central goal of time-series analysis. This review centers on three major developments in recent AI-based time-series forecasting: transformers, large pretrained models for zero-shot forecasting, and diffusion-based generative forecasters. We connect these methods to the econometric tradition built around the vector autoregression (VAR) through a common object: the conditional distribution of the future given the past. The review is organized around three long-standing challenges: high dimensionality, nonstationarity, and nonlinearity. We argue that modern methods make progress by expanding the classical forecasting template: they allow more flexible dynamics, use larger information sets and training corpora, and represent richer predictive distributions. Yet they often lack the inferential and structural tools that make classical models useful for testing, explanation, and policy analysis. We close by outlining open problems where econometric tools remain important. |
| Date: | 2026–01–30 |
| URL: | https://d.repec.org/n?u=RePEc:bri:uobdis:26/838 |