Enrico Gerding

AAMAS Conference 2026 Conference Paper

Alternating-Time Temporal Logic with Dependent Strategies

• Jessica L. Newman
• Enrico Gerding
• Enrico Marchioni
• Baharak Rastegari

Alternating-Time Temporal Logic (ATL) can express statements about the strategic abilities of agents in games where agents move concurrently. However, many game-theoretic scenarios (such as Stackelberg competitions) require agents to make moves sequentially, with the actions of a given agent depending on the actions of the agents who move prior to them. To capture this, we introduce ATL with Dependent Strategies (ATLDS), which extends ATL with the ability to specify an order in which agents select actions. We characterise the sets of outcomes that are possible for a coalition to enforce when playing a normal-form game sequentially, and provide a representation theorem that allows us to convert betweengamesandsetsofenforceableoutcomesgeneratedfromthose games. We use this to give a sound and complete axiomatisation of ATLDS. We also show expressive equivalence with the SL−[SG] fragment of Strategy Logic, and provide complexity bounds for variants of the model-checking problem.

AAMAS Conference 2026 Conference Paper

EVMapSim: A Network-level Electric Vehicle Charging Simulator

• Prokopis Georgiou
• Jayati Deshmukh
• Vahid Yazdanpanah
• Sebastian Stein
• Enrico Gerding

Long-distance electric vehicle (EV) travel depends critically on charging infrastructure reliability. When stations fail or queues form unexpectedly, drivers face increased range anxiety and risk of getting stranded. In this demonstration paper, we present EVMap- Sim, a discrete-event simulator for modelling EV navigation and charging behaviour at a national scale. We demonstrate 10, 000+ vehiclestraversingtheUKroadnetwork, eachmakingreal-timechargingdecisionswhileencounteringinfrastructurefailures. EVMapSim supports three failure scenarios, enabling analysis of how infrastructure resilience affects driver outcomes, including wait times, route deviations, and stranding rates.

AAMAS Conference 2025 Conference Paper

Adaptive Microtolling in Competitive Online Congestion Games via Multiagent Reinforcement Learning

• Behrad Koohy
• Sebastian Stein
• Enrico Gerding

Efficient urban traffic management remains a critical challenge, yet traditional congestion games fail to capture the dynamic and competitive nature of real-world transportation systems. We introduce the Multi-Market Routing Problem (MMRP), an online and oligopolistic extension that models competition amongst route providers utilising adaptive microtolling strategies to influence driver behaviour and mitigate congestion. We formally define the MMRP, highlighting the computational complexity of solving the MMRP, and use an adapted version of Proximal Policy Optimisation (PPO) to improve update stability in multiagent environments to address this problem in online settings. Our empirical analysis demonstrates that our PPO-based approach not only matches the performance of existing benchmarks but also significantly enhances equity, reduces travel times for users, and increases profitability for providers.

AAMAS Conference 2025 Conference Paper

Resource Task Games

• Jessica L. Newman
• Enrico Gerding
• Enrico Marchioni
• Baharak Rastegari

In this work, we introduce Resource Task Games (RTGs), a model of cooperative strategic interactions generalising Wooldridge and Dunne’s Coalitional Resource Games. In RTGs, agents are endowed with different types of resources, which can be put towards graded completion of certain tasks. Agents have preferences over the states of completion of these tasks and can allocate resources in cooperation with other agents. We introduce a notion of core for RTGs and investigate the existence and computation of stable outcomes and core-related closure properties. We show that RTGs are sufficiently expressive to encode Transferable Utility (TU) games efficiently, providing a construction from an arbitrary TU game to an RTG that preserves the core. We provide the computational complexity classes of problems relating to the core of these games, including bounds on the polynomial hierarchy for each problem.

AAMAS Conference 2024 Conference Paper

Adaptive Incentive Engineering in Citizen-Centric AI

• Behrad Koohy
• Jan Buermann
• Vahid Yazdanpanah
• Pamela Briggs
• Paul Pschierer-Barnfather
• Enrico Gerding
• Sebastian Stein

Adaptive incentives are a valuable tool shown to improve the efficiency of complex multiagent systems and could produce win-win situations for all stakeholders. However, their application usage is very limited, partly due to a significant gap between the literature and practice. We argue that overcoming this gap requires addressing four open research challenges. First, the dynamic, volatile and uncertain nature of environments needs to be fully considered. Second, social factors including user acceptance, fairness, ethical considerations and trust have to match end users’ expectations and needs. Third, the evaluation of mechanisms and systems has to be robust and focused on real-world outcomes and stakeholder requirements. Finally, all this has to be built on a reliable theoretical foundation. In order to overcome these open challenges in adaptive incentive engineering, tools from the fields of mechanism design and game theory can be used. This will help to achieve the opportunities adaptive incentives can provide to real-world practical environments, producing better AI systems for the benefit of all.

NeurIPS Conference 2020 Conference Paper

Optimal Learning from Verified Training Data

• Nicholas Bishop
• Long Tran-Thanh
• Enrico Gerding

Standard machine learning algorithms typically assume that data is sampled independently from the distribution of interest. In attempts to relax this assumption, fields such as adversarial learning typically assume that data is provided by an adversary, whose sole objective is to fool a learning algorithm. However, in reality, it is often the case that data comes from self-interested agents, with less malicious goals and intentions which lie somewhere between the two settings described above. To tackle this problem, we present a Stackelberg competition model for least squares regression, in which data is provided by agents who wish to achieve specific predictions for their data. Although the resulting optimisation problem is nonconvex, we derive an algorithm which converges globally, outperforming current approaches which only guarantee convergence to local optima. We also provide empirical results on two real-world datasets, the medical personal costs dataset and the red wine dataset, showcasing the performance of our algorithm relative to algorithms which are optimal under adversarial assumptions, outperforming the state of the art.

AAMAS Conference 2019 Conference Paper

A Truthful Online Mechanism for Allocating Fog Computing Resources

• Fan Bi
• Sebastian Stein
• Enrico Gerding
• NICK JENNINGS
• Tom La Porta

AAMAS Conference 2018 Conference Paper

Coordination of Electric Vehicle Aggregators: A Coalitional Approach

• Alvaro Perez-Diaz
• Enrico Gerding
• Frank McGroarty

Given the rapid rise of electric vehicles (EVs) worldwide, and the ambitious targets set for the near future, the smart charging of an EV fleet must be seen as a priority. Specifically, we study a scenario where EV charging is managed through self-interested EV aggregators (e. g. car parks or electricity suppliers) who compete in the day-ahead market in order to purchase the electricity needed to meet their clients’ requirements. In order to reduce electricity costs and lower the impact on electricity markets, we study the possibility of inter-aggregator cooperation. Specifically, we model the system as a coalitional game and prove that the resulting game is superadditive and balanced, hence having a non-empty core. However, due to the game not being convex, the Shapley value is not guaranteed to lie in the core. As an alternative, we propose employing the payment mechanism provided by the least-core, which we show to be in the core in our setting. Furthermore, a realistic empirical evaluation is presented, using real market and driver data from the Iberian Peninsula. The simulations show that large payment reductions can be achieved when using the coordination mechanism. Moreover, we show that the individual payments of the least-core are very close to the Shapley value, suggesting that the payment mechanism is both fair and stable.

AAMAS Conference 2017 Conference Paper

Adaptive Pricing Mechanisms for On-Demand Mobility

• Maciej Drwal
• Enrico Gerding
• Sebastian Stein
• Keiichiro Hayakawa
• Hironobu Kitaoka

We consider on-demand car rental systems for public transportation. In these systems, demands are often unbalanced across different parking stations, necessitating costly manual relocations of vehicles. To address this so-called “deadheading” effect and maximise the operator’s revenue, we propose two novel pricing mechanisms. These adaptively adjust the prices between origin and destination stations depending on their current occupancy, probabilistic information about the customers’ valuations and estimated relocation costs. In so doing, the mechanisms incentivise drivers to help rebalance the system and place a premium on trips that lead to costly relocations. We evaluate the mechanisms in a series of experiments using real historical data from an existing on-demand mobility system in a French city. We show that our mechanisms achieve an up to 64% increase in revenue for the operator and at the same time up to 36% fewer relocations.

AAAI Conference 2015 Conference Paper

Balanced Trade Reduction for Dual-Role Exchange Markets

• Dengji Zhao
• Sarvapali Ramchurn
• Enrico Gerding
• Nicholas Jennings

We consider dual-role exchange markets, where traders can offer to both buy and sell the same commodity in the exchange but, if they transact, they can only be either a buyer or a seller, which is determined by the market mechanism. To design desirable mechanisms for such exchanges, we show that existing solutions may not be incentive compatible, and more importantly, cause the market maker to suffer a significant deficit. Hence, to combat this problem, following McAfee’s trade reduction approach, we propose a new trade reduction mechanism, called balanced trade reduction, that is incentive compatible and also provides flexible trade-offs between efficiency and deficit.

AAAI Conference 2014 Conference Paper

Mechanism Design for Mobile Geo-Location Advertising

• Nicola Gatti
• Marco Rocco
• Sofia Ceppi
• Enrico Gerding

Mobile geo–location advertising, where mobile ads are targeted based on a user’s location, has been identified as a key growth factor for the mobile market. As with online advertising, a crucial ingredient for their success is the development of effective economic mechanisms. An important difference is that mobile ads are shown sequentially over time and information about the user can be learned based on their movements. Furthermore, ads need to be shown selectively to prevent ad fatigue. To this end, we introduce, for the first time, a user model and suitable economic mechanisms which take these factors into account. Specifically, we design two truthful mechanisms which produce an advertisement plan based on the user’s movements. One mechanism is allocatively efficient, but requires exponential compute time in the worst case. The other requires polynomial time, but is not allocatively efficient. Finally, we experimentally evaluate the trade– off between compute time and efficiency of our mechanisms.

AAMAS Conference 2012 Conference Paper

A Model-Based Online Mechanism with Pre-Commitment and its Application to Electric Vehicle Charging

• Sebastian Stein
• Enrico Gerding
• Valentin Robu
• NICK JENNINGS

We introduce a novel online mechanism that schedules the allocation of an expiring and continuously-produced resource to self-interested agents with private preferences. A key application of our mechanism is the charging of pure electric vehicles, where owners arrive dynamically over time, and each owner requires a minimum amount of charge by its departure to complete its next trip. To truthfully elicit the agents' preferences in this setting, we introduce the new concept of pre-commitment: Whenever an agent is selected, our mechanism pre-commits to charging the vehicle by its reported departure time, but maintains flexibility about \emph{when} the charging takes place and at \emph{what rate}. Furthermore, to make effective allocation decisions we use a model-based approach by modifying Consensus, a well-known online optimisation algorithm. We show that our pre-commitment mechanism with modified Consensus incentivises truthful reporting. Furthermore, through simulations based on real-world data, we show empirically that the average utility achieved by our mechanism is 93% or more of the offline optimal.

AAMAS Conference 2012 Conference Paper

A Scoring Rule-based Mechanism for Aggregate Demand Prediction in the Smart Grid

• Harry Rose
• Alex Rogers
• Enrico Gerding

This paper presents a novel scoring rule-based strictly dominant incentive compatible mechanism that encourages agents to produce costly estimates of future events and report them truthfully to a centre. Whereas prior work has assumed a fixed budget for payment towards agents, this work makes use of prior information held by the centre and assumes a budget that is determined by the savings made through the use of the agents' information over the centre's own prior information. This mechanism is compared to a simple benchmark mechanism wherein the savings are divided equally among all home agents, and a cooperative solution wherein agents act to maximise social welfare. Empirical analysis is performed in which the mechanism is applied to a simulation of the smart grid whereby an aggregator agent must use home agents' information to optimally purchase electricity. It is shown that this mechanism achieves up to 77% of the social welfare achieved by the cooperative solution.

AAMAS Conference 2012 Conference Paper

Merging Multiple Information Sources in Federated Sponsored Search Auctions

• Sofia Ceppi
• Enrico Gerding
• Nicola Gatti

The recent increase of domain–specific search engines, able to discover information unknown by general–purpose search engines, leads to their federation into a single entity, called federated search engine. In this paper, we focus on how it can effectively merge sponsored search results, provided by the domain–specific search engines, into a unique list. In particular, we discuss the case in which the same ad can be provided by multiple sources, which requires information about the ad to be merged. We approach the problem of merging and sharing the revenue using mechanism design techniques. The main impossibility result we obtain points out there exists no mechanism that satisfies the customarily required properties. Thus, we present several mechanisms that violate at most one of these properties, and we experimentally analyze them using a real–world Yahoo! dataset.

AAAI Conference 2011 Conference Paper

Mechanism Design for Federated Sponsored Search Auctions

• Sofia Ceppi
• Nicola Gatti
• Enrico Gerding

Recently there is an increase in smaller, domain–specific search engines that scour the deep web finding information that general–purpose engines are unable to discover. These search engines play a crucial role in the new generation of search paradigms where federated search engines (FSEs) integrate search results from heterogeneous sources. In this paper we pose, for the first time, the problem to design a revenue mechanism that ensures profits both to individual search engines and FSEs as a mechanism design problem. To this end, we extend the sponsored search auction models and we discuss possibility and impossibility results on the implementation of an incentive compatible mechanism. Specifically, we develop an execution–contingent VCG (where payments depend on the observed click behavior) that satisfies both individual rationality and weak budget balance in expectation.

AAMAS Conference 2010 Conference Paper

A Game-Theoretic Analysis of Market Selection Strategies for Competing Double Auction Marketplaces

• Bing Shi
• Enrico Gerding
• Perukrishnen Vytelingum
• Nicholas R. Jennings

In this paper, we propose a novel general framework foranalysing competing double auction markets that vie fortraders, who then need to choose which market to go to. Based on this framework, we analyse the competition between two markets in detail. Specifically, we game-theoretically analyse the equilibrium behaviour of traders' marketselection strategies and adopt evolutionary game theory toinvestigate how traders dynamically change their strategies, and thus, which equilibrium, if any, can be reached. In sodoing, we show that it is unlikely for these competing markets to coexist. Eventually, all traders will always convergeto locating themselves at one of the markets. Somewhatsurprisingly, we find that sometimes all traders converge tothe market that charges higher fees. Thus we further analyse this phenomenon, and specifically determine the factorsthat affect such migration.

AAMAS Conference 2010 Conference Paper

Flexibly Priced Options: A New Mechanism for Sequential Auction Markets with Complementary Goods

• Valentin Robu
• Ioannis Vetsikas
• Enrico Gerding
• Nicholas R. Jennings

In this work, we propose a novel option pricing mechanism for reducing the exposure problem encountered by bidders with complementary valuations when participating in sequential, second-priceauction markets. In our mechanism, both the option and the exercise price are determined dynamically, by the bids received in eachauction. We show that our flexible options model can achieve bettermarket allocation efficiency, at an only marginal cost to seller revenues compared to existing state of the art option pricing models.

AAMAS Conference 2010 Conference Paper

Scalable Mechanism Design for the Procurement of Services with Uncertain Durations

• Enrico Gerding
• Sebastian Stein
• Kate Larson
• Alex Rogers
• Nicholas R. Jennings

In this paper, we study a service procurement problem with uncertainty as to whether service providers are capable of completing agiven task within a specified deadline. This type of setting is often encountered in large and dynamic multi-agent systems, such ascomputational Grids or clouds. To effectively deal with this uncertainty, the consumer may dynamically and redundantly procuremultiple services over time, in order to increase the probability ofsuccess, while at the same time balancing this with the additionalprocurement costs. However, in order to do this optimally, the consumer requires information about the providers' costs and their success probabilities over time. This information is typically held privately by the providers and they may have incentives to misreportthis, so as to increase their own profits. To address this problem, weintroduce a novel mechanism that incentivises self-interested providers to reveal their true costs and capabilities, and we show thatthis mechanism is ex-post incentive compatible, efficient and individually rational. However, for these properties to hold, it generallyneeds to compute the optimal solution, which can be intractable inlarge settings. Therefore, we show how we can generate approximate solutions while maintaining the economic properties of themechanism. This approximation admits a polynomial-time solution that can be computed in seconds even for hundreds of providers, and we demonstrate empirically that it performs as well as theoptimal in typical scenarios. In particularly challenging settings, we show that it still achieves 97\% or more of the optimal.

IJCAI Conference 2009 Conference Paper

• Sebastian Stein
• Enrico Gerding
• Alex Rogers
• Kate Larson
• Nicholas R. Jennings

Emerging service-oriented technologies allow software agents to automatically procure distributed services to complete complex tasks. However, in many application scenarios, service providers demand financial remuneration, execution times are uncertain and consumers have deadlines for their tasks. In this paper, we address these issues by developing a novel approach that dynamically procures multiple, redundant services over time, in order to ensure success by the deadline. Specifically, we first present an algorithm for finding optimal procurement solutions, as well as a heuristic algorithm that achieves over 99% of the optimal and is capable of handling thousands of providers. Using experiments, we show that these algorithms achieve an improvement of up to 130% over current strategies that procure only single services. Finally, we consider settings where service costs are not known to the consumer, and introduce several mechanisms that incentivise providers to reveal their costs truthfully and that still achieve up to 95% efficiency.

IJCAI Conference 2009 Conference Paper

• Zinovi Rabinovich
• Enrico Gerding
• Maria Polukarov
• Nicholas R. Jennings

Recently, efficient approximation algorithms for finding Nash equilibria have been developed for the interesting class of anonymous games, where a player’s utility does not depend on the identity of its opponents. In this paper, we tackle the problem of computing equilibria in such games with continuous player types, extending the framework to encompass settings with imperfect information. In particular, given the existence result for pure Bayes-Nash equilibiria in these games, we generalise the fictitious play algorithm by developing a novel procedure for finding a best response strategy, which is specifically designed to deal with continuous and, therefore, infinite type spaces. We then combine the best response computation with the general fictitious play structure to obtain an equilibrium. To illustrate the power of this approach, we apply our algorithm to the domain of simultaneous auctions with continuous private values and discrete bids, in which the algorithm shows quick convergence.

AAMAS Conference 2008 Conference Paper

An Agent-Based Electrical Power Market

• Jaime Cerda Jacobo
• David De Roure
• Enrico Gerding

This demonstration shows an agent-based model for the electricity power market, in which the optimal power flow is determined in a bottom-up fashion. Here, each agent controls a single electrical node consisting of several power generators, loads (consumer demand), and is connected to neighbouring nodes through transmission lines. Furthermore, each of the components has associated physical constraints, such as the line and generators’ capacities. Through a process resembling tatonnement in markets, the optimal system solution which maximises social welfare is reached within a few iterations. The demonstrator visualises this process and also shows how the various constraints affect the system behaviour and how this changes with different settings.

AAMAS Conference 2008 Conference Paper

Approximating Mixed Nash Equilibria using Smooth Fictitious Play in Simultaneous Auctions

• Enrico Gerding
• Andrew Byde
• Edith Elkind
• Zinovi Rabinovich
• NICK JENNINGS

AAMAS Conference 2008 Conference Paper

Characterizing effective auction mechanisms: Insights from the 2007 TAC market design competition

• Jinzhong Niu
• Kai Cai
• Enrico Gerding
• Peter McBurney
• Simon Parsons

This paper analyzes the entrants to the 2007 TAC Market Design competition. It presents a classification of the entries to the competition, and uses this classification to compare these entries. The paper also attempts to relate market dynamics to the auction rules adopted by these entries and their adaptive strategies via a set of post-tournament experiments. Based on this analysis, the paper speculates about the design of effective auction mechanisms, both in the setting of this competition and in the more general case.

AAMAS Conference 2008 Conference Paper

JCAT: A Platform for the TAC Market Design Competition

• Jinzhong Niu
• Kai Cai
• Enrico Gerding
• Peter McBurney
• Thierry Moyaux
• Steve Phelps
• Dave Shield
• Simon Parsons

AAMAS Conference 2006 Conference Paper

Competing Sellers in Online Markets: Reserve Prices, Shill Bidding, and Auction Fees

• Enrico Gerding
• Alex Rogers
• Rajdeep Dash
• NICK JENNINGS