Research | Nicolas Eschenbaum

My research sits at the intersection of AI, industrial organization, and competition policy — digital markets, market design, energy platforms, and AI regulation, studied with game theory, empirical methods, and machine learning.

2026

arXiv
Shared Bidding Algorithms and Competition: Evidence from Electricity Markets

Nicolas Eschenbaum

Aug 2026

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Competing firms increasingly delegate market decisions to algorithms supplied by the same third-party providers. We study whether a shared algorithm leads competitors to internalise one another’s profits, using data from the Australian National Electricity Market, where batteries’ bids are observed at 5-minute frequency and can be linked to an autobidding provider. Bids constructed by the same provider co-move, and do so more strongly after a disclosure reform made the scarcity state easier to observe: the same information that steers batteries towards efficient arbitrage also synchronises the bids of competitors who share a provider. To separate co-movement from joint profit maximisation, we perform a conduct test by estimating each battery’s dynamic value of stored energy and reclearing the market under counterfactual bids. We find that batteries forgo profitable dispatch in the evening peak when it would lower the profit of same-provider batteries owned by rival firms. The estimated conduct parameter is close to one. But this effect arises only where a provider’s share of near-margin battery capacity exceeds roughly 30%. The identified conduct costs consumers an annualised $5.5 million given the battery fleet from 2025, and concentration analysis based on ownership would treat these batteries as independent competitors and miss the impact of shared autobidding software.
@unpublished{eschenbaum-shared-bidding-2026, author = {Eschenbaum, Nicolas}, title = {Shared Bidding Algorithms and Competition: Evidence from Electricity Markets}, month = aug, year = {2026}, keywords = {Bidding algorithms, Algorithmic competition, Electricity markets, Collusion}, }

Algorithmic Pricing and Collusion in Practice

Thomas K. Cheng, Nicolas Eschenbaum, Peter Georg Picht, and 1 more author

Aug 2026

Bib

@unpublished{cheng-eschenbaum-picht-talluri-2026,
  author = {Cheng, Thomas K. and Eschenbaum, Nicolas and Picht, Peter Georg and Talluri, Kalyan},
  title = {Algorithmic Pricing and Collusion in Practice},
  year = {2026},
  keywords = {Algorithmic pricing, Algorithmic collusion, Competition policy, Revenue management},
}

WP
Auditing Algorithmic Collusion from Strategy Graphs

Nicolas Eschenbaum, and Janusz M. Meylahn

Aug 2026

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Detecting algorithmic collusion is challenging because regulators often have limited access to firms’ algorithms, training data, and market information. We study an intermediate-information regime in which an auditor can query firms’ frozen pricing policies and construct the induced strategy graph. Using a complete characterization of Nash equilibria in a repeated pricing game, we identify graph-theoretic features of strategy graphs that are associated with collusive reward-and-punishment schemes, including maximum betweenness, attractor in-degree, and average path length. We then test these metrics on policies learned by decentralized Q-learning and the Q-learning algorithm of Calvano et al. (2020). We find that especially the maximum betweenness and attractor in-degree are strongly correlated with the standard profit-based Collusion Index. Importantly, the proposed metrics rely only on the unlabeled topology of strategy graphs and require neither price histories, demand estimates, nor competitive and monopoly benchmarks. Our results suggest that the structure of frozen pricing policies contains robust signals of collusion among reinforcement learning algorithms and provides a promising basis for auditing algorithmic pricing systems under limited information.
@unpublished{eschenbaum-meylahn-strategy-graphs-2026, author = {Eschenbaum, Nicolas and Meylahn, Janusz M.}, title = {Auditing Algorithmic Collusion from Strategy Graphs}, month = aug, year = {2026}, keywords = {Algorithmic collusion, Auditing, Reinforcement learning, Competition policy}, }
Energy & AI
Peer-to-peer electricity platforms with endogenous prices: A Multi-Agent Neural Network Control approach

Nicolas Greber, Nicolas Eschenbaum, and Oleg Szehr

Energy and AI, Aug 2026

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The transition towards decentralised energy systems creates a need for local electricity platforms on which prosumers trade energy at endogenously determined prices. We develop a differentiable multi-agent control framework for such platforms, drawing on neural stochastic control and the pathwise optimisation principle used in Deep Hedging, whose appeal stems from its simplicity, computational efficiency, industrial use, and scalability to large-scale optimisation problems. The proposed approach embeds physical storage dynamics, feasible prosumer actions, and the market-clearing rule in a single computational graph, allowing decentralised agents to train trading policies by backpropagating through aggregate supply, aggregate demand, and cleared prices. A stop-gradient construction at the clearing layer separates two economically distinct learning modes: price-aware learning, in which agents internalise their marginal price impact, and price-taking learning, in which prices are treated as exogenous. This yields a market-mechanism-aware alternative to reward-only multi-agent reinforcement learning methods such as Multi-Agent Proximal Policy Optimisation, which treat the market as a black-box environment component. We evaluate the framework on peer-to-peer electricity markets with heterogeneous prosumers, photovoltaic generation, battery storage, and multiple non-discriminatory pricing rules, using both large-scale synthetic simulations and Swiss smart-meter demand data. The experiments show that Multi-Agent Neural Network Control learns effective decentralised policies, reduces community cost relative to rule-based control, and outperforms Multi-Agent Proximal Policy Optimisation in the studied settings in both cost and training efficiency. The learned policies exhibit emergent, economically sensible behaviour: they recover standard storage arbitrage and, under price-aware learning, reveal capacity withholding under quantity-sensitive clearing, which benefits flexible agents at the expense of aggregate community cost. Embedding market clearing in the training graph therefore provides both an efficient learning signal and a diagnostic tool for strategic behaviour in local electricity-market design.
@article{greber-eschenbaum-szehr-2026, author = {Greber, Nicolas and Eschenbaum, Nicolas and Szehr, Oleg}, journal = {Energy and AI}, title = {Peer-to-peer electricity platforms with endogenous prices: A Multi-Agent Neural Network Control approach}, year = {2026}, keywords = {Peer-to-peer (P2P) electricity trading, Energy platforms, Multi-agent reinforcement learning, Neural networks, Market design}, doi = {10.1016/j.egyai.2026.100809}, volume = {25}, pages = {100809}, }
arXiv
Market Power and Platform Design in Decentralized Electricity Trading

Nicolas Eschenbaum, and Nicolas Greber

Mar 2026

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This paper studies how platform design shapes strategic behavior in decentralized electricity trading. We develop a finite-horizon dynamic game in which photovoltaic- and battery-equipped players ("prosumers") trade on a platform that maps aggregate imports and exports into internal buy and sell prices. We establish existence of a perfect conditional epsilon-equilibrium and characterize a Cournot-like market-power mechanism in an observable-types benchmark of the game: because the producer price is decreasing in aggregate exports, strategic prosumers withhold supply and underutilize storage relative to the price-taking benchmark. To quantify these effects, we use a multi-agent computational framework that exploits the differentiable structure of the platform’s clearing rule to compare planner, price-taking, and strategic outcomes under alternative pricing mechanisms. In our baseline calibration, strategic play raises grid settlement cost by about 6 percent relative to price-taking. The magnitude of the distortion depends strongly on platform design: some designs can largely eliminate strategic incentives, while increased competition in storage ownership sharply reduces withholding, with most of the distortion disappearing once storage is split across more than three owners. We also find that information disclosure can improve competitive coordination but also increase the market power effects. Despite these distortions, the platform remains highly valuable overall, reducing a passive consumer’s annual electricity bill by roughly 40 percent relative to exclusive grid settlement, with strategic behavior clawing back only about 8 percent of that saving. The results show that pricing rules, information disclosure, and ownership structure determine how much of the gains from decentralized electricity trading are realized.
@unpublished{eschenbaum-greber-marketpower-2026, author = {Eschenbaum, Nicolas and Greber, Nicolas}, title = {Market Power and Platform Design in Decentralized Electricity Trading}, month = mar, year = {2026}, keywords = {Peer-to-peer electricity trading, Market power, Platform design, Market design}, }
WC
Selective Confusion: An Empirical Analysis of the DMA’s Brussels Effect

Peter Georg Picht, Luka Nenadic, Octavia Barnes, and 2 more authors

World Competition, Mar 2026

Forthcoming

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This article examines the extent to which designated “gatekeepers” implement the provisions of the EU’s Digital Markets Act outside its territorial scope ("Brussels Effect"). Drawing on transparency reports, contractual documents, and informal communications, we reveal significant disparities in compliance strategies. Apple, Google, and Booking predominantly restrict their implementation to the EU or EEA, whereas Microsoft, Meta, and ByteDance extend certain measures to non-EU jurisdictions, notably Switzerland. Crucially, obligations subject to non-compliance proceedings by the European Commission are rarely extended beyond the EU, suggesting a strategic approach to territorial extensions of the DMA’s implementation. The article also uncovers a pattern of complex and sometimes contradictory communication by gatekeepers, raising questions about the transparency of the DMA’s implementation. These inconsistencies, coupled with selective extensions of specific data-related DMA provisions, point to a fragmented “Brussels Effect” of the law. The findings also imply that gatekeepers weigh the economic and strategic costs of compliance when deciding on territorial scope, and that the DMA’s global impact may depend on further coordination between regulators as well as more stringent enforcement.
@article{picht-etal-selective-confusion-2025, author = {Picht, Peter Georg and Nenadic, Luka and Barnes, Octavia and Eschenbaum, Nicolas and Kuster, Yannick}, journal = {World Competition}, title = {Selective Confusion: An Empirical Analysis of the DMA's Brussels Effect}, year = {2026}, note = {Forthcoming}, keywords = {Digital Markets Act, gatekeepers, Brussels Effect, digital platforms, Apple, Google, enforcement, interoperability, compliance strategies, strategic ambiguity}, }
arXiv
A Unified Framework for Dynamic Monopoly Pricing: Trading-Up

Stefan Buehler, Nicolas Eschenbaum, and Severin Lenhard

Aug 2026

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This paper develops a unified analytical framework for dynamic monopoly pricing that allows for multiple durable, rental, or mixed varieties. We show that trading-up opportunities are the driving force behind Coasian-type price dynamics: prices adjust over time to reallocate buyer types to higher-valued consumption options. Price adjustments persist until these opportunities are exhausted or the game ends. When trading-up opportunities are absent, equilibrium prices are static, resulting in Coasian failure. We illustrate the mechanism with examples and extend the analysis to transitional games in which one variety is only indirectly accessible.
@unpublished{buehler-eschenbaum-lenhard-2025, author = {Buehler, Stefan and Eschenbaum, Nicolas and Lenhard, Severin}, title = {A Unified Framework for Dynamic Monopoly Pricing: Trading-Up}, month = aug, year = {2026}, }

2025

SZW

DMA (Verw-)Irrungen: Was die territorialen Umsetzungsstrategien der Torwächter über den Digital Markets Act lehren

Peter Georg Picht, Luka Nenadic, Octavia Barnes, and 2 more authors

Schweizerische Zeitschrift für Wirtschafts- und Finanzmarktrecht (SZW), Aug 2025

Bib HTML

@article{picht-etal-szw-2025,
  author = {Picht, Peter Georg and Nenadic, Luka and Barnes, Octavia and Eschenbaum, Nicolas and Kuster, Yannick},
  journal = {Schweizerische Zeitschrift für Wirtschafts- und Finanzmarktrecht (SZW)},
  title = {DMA (Verw-)Irrungen: Was die territorialen Umsetzungsstrategien der Torwächter über den Digital Markets Act lehren},
  year = {2025},
  number = {4},
  keywords = {Digital Markets Act, gatekeepers, Brussels Effect, territorial scope, enforcement},
  url = {https://www.szw.ch/de/artikel/2504-0685-2025-0028/dma-verw-irrungen},
}

WP
Dynamic Pricing in Bilateral Relationships: Experimental Evidence

Stefan Buehler, Thomas F. Epper, Nicolas Eschenbaum, and 1 more author

Sep 2025

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This paper presents experimental evidence on dynamic pricing in finite-horizon bilateral relationships, building on Hart and Tirole (1988). We examine four distinct treatments that vary in terms of the mode of trade and the seller’s commitment ability. Our findings demonstrate that theory accurately predicts average prices but fails to account for the diversity of individual price trajectories. Contrary to theoretical predictions, commitment has little influence on dynamic pricing, with sellers leaving significant rents to buyers and frequently committing to changing or oscillating prices. In the absence of commitment, strategic behavior is prevalent among sellers but not necessarily among buyers.
@unpublished{dynamic-pricing-experiment-2025, author = {Buehler, Stefan and Epper, Thomas F. and Eschenbaum, Nicolas and Koch, Roberta}, title = {Dynamic Pricing in Bilateral Relationships: Experimental Evidence}, month = sep, year = {2025}, }
arXiv
Repeated Auctions with Speculators: Arbitrage Incentives and Forks in DAOs

Nicolas Eschenbaum, and Nicolas J. Greber

May 2025

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We analyze the vulnerability of decentralized autonomous organizations (DAOs) to speculative exploitation via their redemption mechanisms. Studying a game-theoretic model of repeated auctions for governance shares with speculators, we characterize the conditions under which—in equilibrium—an exploitative exit is guaranteed to occur, occurs in expectation, or never occurs. We evaluate four redemption mechanisms and extend our model to include atomic exits, time delays, and DAO spending strategies. Our results highlight an inherent tension in DAO design: mechanisms intended to protect members from majority attacks can inadvertently create opportunities for costly speculative exploitation. We highlight governance mechanisms that can be used to prevent speculation.
@unpublished{eschenbaum-greber-daos-2025, author = {Eschenbaum, Nicolas and Greber, Nicolas J.}, title = {Repeated Auctions with Speculators: Arbitrage Incentives and Forks in DAOs}, month = may, year = {2025}, }

2024

sic!

Schweizer DMA-Brussels-Effect? Wie Gatekeeper den DMA in der Schweiz (nicht) umsetzen

Peter Georg Picht, Luka Nenadic, Octavia Barnes, and 2 more authors

sic! Zeitschrift für Immaterialgüter-, Informations- und Wettbewerbsrecht, May 2024

Bib HTML

@article{picht-etal-sic-2024,
  author = {Picht, Peter Georg and Nenadic, Luka and Barnes, Octavia and Eschenbaum, Nicolas and Kuster, Yannick},
  journal = {sic! Zeitschrift für Immaterialgüter-, Informations- und Wettbewerbsrecht},
  title = {Schweizer DMA-Brussels-Effect? Wie Gatekeeper den DMA in der Schweiz (nicht) umsetzen},
  year = {2024},
  pages = {3 - 14},
  keywords = {Digital Markets Act, gatekeepers, Brussels Effect, Switzerland, platform regulation},
  url = {https://www.zora.uzh.ch/entities/publication/763e6f62-f9a4-440e-9187-11f3ba77cac7},
}

2022

arXiv
Robust Algorithmic Collusion

Nicolas Eschenbaum, Filip Mellgren, and Philipp Zahn

Jan 2022

Presented at the NeurIPS PERLS workshop

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This paper develops a formal framework to assess policies of learning algorithms in economic games. We investigate whether reinforcement-learning agents with collusive pricing policies can successfully extrapolate collusive behavior from training to the market. We find that in testing environments collusion consistently breaks down. Instead, we observe static Nash play. We then show that restricting algorithms’ strategy space can make algorithmic collusion robust, because it limits overfitting to rival strategies. Our findings suggest that policy-makers should focus on firm behavior aimed at coordinating algorithm design in order to make collusive policies robust.
@unpublished{eschenbaum-mellgren-zahn-2022, author = {Eschenbaum, Nicolas and Mellgren, Filip and Zahn, Philipp}, title = {Robust Algorithmic Collusion}, month = jan, year = {2022}, note = {Presented at the NeurIPS PERLS workshop}, }

2021

arXiv
Dealing with Uncertainty: The Value of Reputation in the Absence of Legal Institutions

Nicolas Eschenbaum, and Helge Liebert

Jul 2021

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This paper studies reputation in the online market for illegal drugs in which no legal institutions exist to alleviate uncertainty. Trade takes place on platforms that offer rating systems for sellers, thereby providing an observable measure of reputation. The analysis exploits the fact that one of the two dominant platforms unexpectedly disappeared. Re-entering sellers reset their rating. The results show that on average prices decreased by up to 9% and that a 1% increase in rating causes a price increase of 1%. Ratings and prices recover after about three months. We calculate that identified good types earn 1,650 USD more per week.
@unpublished{eschenbaum-liebert-2021, author = {Eschenbaum, Nicolas and Liebert, Helge}, title = {Dealing with Uncertainty: The Value of Reputation in the Absence of Legal Institutions}, month = jul, year = {2021}, keywords = {Reputation, institutions, uncertainty, dark web, drugs}, }

2020

JEBO
Explaining escalating prices and fines: A unified approach

Stefan Buehler, and Nicolas Eschenbaum

Journal of Economic Behavior & Organization, Jul 2020

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This paper provides an explanation for escalating prices and fines based on a unified analytical framework that nests monopoly pricing and optimal law enforcement. We show that escalation emerges as an optimal outcome if the principal (i) lacks commitment ability, and (ii) gives less than full weight to agent benefits. Escalation is driven by decreasing transfers for non-active agents rather than increasing transfers for active agents. Some forward-looking agents then strategically delay their activity, which drives a wedge between the optimal static transfer and the benefit of an indifferent agent. This wedge is the source of escalation.
@article{buehler-eschenbaum-2020, author = {Buehler, Stefan and Eschenbaum, Nicolas}, journal = {Journal of Economic Behavior & Organization}, title = {Explaining escalating prices and fines: A unified approach}, year = {2020}, issn = {0167-2681}, pages = {153 - 164}, volume = {171}, doi = {10.1016/j.jebo.2020.01.008}, keywords = {Escalation, Behavior-based pricing, Repeat offenders, Deterrence}, url = {http://www.sciencedirect.com/science/article/pii/S0167268120300093}, }

2018

WP
Estimating Geographic Market Size Nonparametrically: An Application to Grocery Retailing

Nicolas Eschenbaum

Nov 2018

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This paper develops a nonparametric approach to empirically determine geographic market size. I exploit highly detailed spatial data and provide estimates of business-stealing effects across distance by studying the impact of store entry on competitors in an increasing range to the entry site. Entropy balancing is employed to control for systematic differences across local markets. I estimate that markets for Swiss grocery retailing stores are highly localized in a tight four kilometer radius. I further document evidence that the impact weakens with increasing distance and that smaller retailers compete in a more narrow market of only two kilometers in size.
@unpublished{eschenbaum-2018, author = {Eschenbaum, Nicolas}, title = {Estimating Geographic Market Size Nonparametrically: An Application to Grocery Retailing}, month = nov, year = {2018}, }