Ben Berger, Edward W. Felten, Akaki Mamageishvili
et al.
Optimistic rollups rely on fraud proofs -- interactive protocols executed on Ethereum to resolve conflicting claims about the rollup's state -- to scale Ethereum securely. To mitigate against potential censorship of protocol moves, fraud proofs grant participants a significant time window, known as the challenge period, to ensure their moves are processed on chain. Major optimistic rollups today set this period at roughly one week, mainly to guard against strong censorship that undermines Ethereum's own crypto-economic security. However, other forms of censorship are possible, and their implication on optimistic rollup security is not well understood. This paper considers economic censorship attacks, where an attacker censors the defender's transactions by bribing block proposers. At each step, the attacker can either censor the defender -- depleting the defender's time allowance at the cost of the bribe -- or allow the current transaction through while conserving funds for future censorship. We analyze three game theoretic models of these dynamics and determine the challenge period length required to ensure the defender's success, as a function of the number of required protocol moves and the players' available budgets.
As artificial intelligence (AI) becomes foundational to enterprise infrastructure, organizations face growing challenges in accurately assessing the full economic implications of AI deployment. Existing metrics such as API token costs, GPU-hour billing, or Total Cost of Ownership (TCO) fail to capture the complete lifecycle costs of AI systems and provide limited comparability across deployment models. This paper introduces the Levelized Cost of Artificial Intelligence (LCOAI), a standardized economic metric designed to quantify the total capital (CAPEX) and operational (OPEX) expenditures per unit of productive AI output, normalized by valid inference volume. Analogous to established metrics like LCOE (levelized cost of electricity) and LCOH (levelized cost of hydrogen) in the energy sector, LCOAI offers a rigorous, transparent framework to evaluate and compare the cost-efficiency of vendor API deployments versus self-hosted, fine-tuned models. We define the LCOAI methodology in detail and apply it to three representative scenarios, OpenAI GPT-4.1 API, Anthropic Claude Haiku API, and a self-hosted LLaMA-2-13B deployment demonstrating how LCOAI captures critical trade-offs in scalability, investment planning, and cost optimization. Extensive sensitivity analyses further explore the impact of inference volume, CAPEX, and OPEX variability on lifecycle economics. The results illustrate the practical utility of LCOAI in procurement, infrastructure planning, and automation strategy, and establish it as a foundational benchmark for AI economic analysis. Policy implications and areas for future refinement, including environmental and performance-adjusted cost metrics, are also discussed.
This note presents a novel and efficient Economic Model Predictive Control (EMPC) scheme specifically designed for non-dissipative systems subject to state and input constraints. To address the stability challenge of EMPC for constrained non-dissipative systems, a new concept of convergence filters is introduced. Three alternative convergence filters are designed accordingly to be incorporated into the receding horizon optimization problem of EMPC. To improve online computational efficiency, the variable horizon approach without explicit terminal state constraints is adopted. This design allows for a flexible trade-off among convergence speed, economic performance, and computational burden via simple parameter adjustment. Moreover, sufficient conditions are rigorously derived to guarantee recursive feasibility and stability. The advantages of the proposed EMPC are validated through simulations on a classical non-dissipative continuous stirred-tank reactor.
María Cerezo-Magaña, Alberto Olivares, Ernesto Staffetti
In this article, the formation mission design problem for commercial aircraft is studied. Given the departure times and the departure and arrival locations of several commercial flights, the relevant weather forecast, and the expected fuel savings during formation flight, the problem consists in establishing how to organize them in formation or solo flights and in finding the trajectories that minimize the overall direct operating cost of the flights. Each aircraft can fly solo or in different positions inside a formation. Therefore, the mission is modeled as a switched dynamical system, in which the discrete state describes the combination of flight modes of the individual aircraft and logical constraints in disjunctive form establish the switching logic among the discrete states of the system. The formation mission design problem has been formulated as an optimal control problem of a switched dynamical system and solved using an embedding approach, which allows switching decision among discrete states to be modeled without relying on binary variables. The resulting problem is a classical optimal control problem which has been solved using a knotting pseudospectral method. Several numerical experiments have been conducted to demonstrate the effectiveness of this approach. The obtained results show that formation flight has great potential to reduce fuel consumption and emissions.
Wenbo Chen, Mathieu Tanneau, Pascal Van Hentenryck
The paper proposes a novel End-to-End Learning and Repair (E2ELR) architecture for training optimization proxies for economic dispatch problems. E2ELR combines deep neural networks with closed-form, differentiable repair layers, thereby integrating learning and feasibility in an end-to-end fashion. E2ELR is also trained with self-supervised learning, removing the need for labeled data and the solving of numerous optimization problems offline. E2ELR is evaluated on industry-size power grids with tens of thousands of buses using an economic dispatch that co-optimizes energy and reserves. The results demonstrate that the self-supervised E2ELR achieves state-of-the-art performance, with optimality gaps that outperform other baselines by at least an order of magnitude.
The view that altruistic punishment plays an important role in supporting public cooperation among human beings and other species has been widely accepted by the public. However, the positive role of altruistic punishment in enhancing cooperation will be undermined if corruption is considered. Recently, behavioral experiments have confirmed this finding and further investigated the effects of the leader's punitive power and the economic potential. Nevertheless, there are relatively few studies focusing on how these factors affect the evolution of cooperation from a theoretical perspective. Here, we combine institutional punishment public goods games with bribery games to investigate the effects of the above factors on the evolution of cooperation. Theoretical and numerical results reveal that the existence of corruption will reduce the level of cooperation when cooperators are more inclined to provide bribes. In addition, we demonstrate that stronger leader and richer economic potential are both important to enhance cooperation. In particular, when defectors are more inclined to provide bribes, stronger leaders can sustain the contributions of public goods from cooperators if the economic potential is weak.
We review the literature on models that try to explain human behavior in social interactions described by normal-form games with monetary payoffs. We start by covering social and moral preferences. We then focus on the growing body of research showing that people react to the language in which actions are described, especially when it activates moral concerns. We conclude by arguing that behavioral economics is in the midst of a paradigm shift towards language-based preferences, which will require an exploration of new models and experimental setups.
Hairo U. Miranda Belmonte, Victor Muñiz-Sánchez, Francisco Corona
Quantification of economic uncertainty is a key concept for the prediction of macro economic variables such as gross domestic product (GDP), and it becomes particularly relevant on real-time or short-time predictions methodologies, such as nowcasting, where it is required a large amount of time series data, commonly with different structures and frequencies. Most of the data comes from the official agencies statistics and non-public institutions, however, relying our estimates in just the traditional data mentioned before, have some disadvantages. One of them is that economic uncertainty could not be represented or measured in a proper way based solely in financial or macroeconomic data, another one, is that they are susceptible to lack of information due to extraordinary events, such as the current COVID-19 pandemic. For these reasons, it is very common nowadays to use some non-traditional data from different sources, such as social networks or digital newspapers, in addition to the traditional data from official sources. The economic policy uncertainty (EPU) index, is the most used newspaper-based indicator to quantify the uncertainty, and is based on topic modeling of newspapers. In this paper, we propose a methodology to estimate the EPU index, which incorporates a fast and efficient method for topic modeling of digital news based on semantic clustering with word embeddings, allowing to update the index in real-time, which is a drawback with another proposals that use computationally intensive methods for topic modeling, such as Latent Dirichlet Allocation (LDA). We show that our proposal allow us to update the index and significantly reduces the time required for new document assignation into topics.
Background The COVID-19 pandemic has increased mental distress globally. The proportion of people reporting anxiety is 26%, and depression is 34% points. Disentangling associational and causal contributions of behavior, COVID-19 cases, and economic distress on mental distress will dictate different mitigation strategies to reduce long-term pandemic-related mental distress. Methods We use the Household Pulse Survey (HPS) April 2020 to February 2021 data to examine mental distress among U.S. citizens attributable to COVID-19. We combined HPS survey data with publicly available state-level weekly: COVID-19 case and death data from the Centers for Disease Control, public policies, and Apple and Google mobility data. Finally, we constructed economic and mental distress measures to estimate structural models with lag dependent variables to tease out public health policies' associational and causal path coefficients on economic and mental distress. Findings From April 2020 to February 2021, we found that anxiety and depression had steadily climbed in the U.S. By design, mobility restrictions primarily affected public health policies where businesses and restaurants absorbed the biggest hit. Period t-1 COVID-19 cases increased job loss by 4.1% and economic distress by 6.3% points in the same period. Job-loss and housing insecurity in t-1 increased period t mental distress by 29.1% and 32.7%, respectively. However, t-1 food insecurity decreased mental distress by 4.9% in time t. The pandemic-related potential causal path coefficient of period t-1 economic distress on period t depression is 57.8%, and anxiety is 55.9%. Thus, we show that period t-1 COVID-19 case information, behavior, and economic distress may be causally associated with pandemic related period t mental distress.
Johannes Köhler, Matthias A. Müller, Frank Allgöwer
In this paper, we consider the problem of periodic optimal control of nonlinear systems subject to online changing and periodically time-varying economic performance measures using model predictive control (MPC). The proposed economic MPC scheme uses an online optimized artificial periodic orbit to ensure recursive feasibility and constraint satisfaction despite unpredictable changes in the economic performance index. We demonstrate that the direct extension of existing methods to periodic orbits does not necessarily yield the desirable closed-loop economic performance. Instead, we carefully revise the constraints on the artificial trajectory, which ensures that the closed-loop average performance is no worse than a locally optimal periodic orbit. In the special case that the prediction horizon is set to zero, the proposed scheme is a modified version of recent publications using periodicity constraints, with the important difference that the resulting closed loop has more degrees of freedom which are vital to ensure convergence to an optimal periodic orbit. In addition, we detail a tailored offline computation of suitable terminal ingredients, which are both theoretically and practically beneficial for closed-loop performance improvement. Finally, we demonstrate the practicality and performance improvements of the proposed approach on benchmark examples.
To battle with economic challenges during the COVID-19 pandemic, the US government implemented various measures to mitigate economic loss. From issuance of stimulus checks to reopening businesses, consumers had to constantly alter their behavior in response to government policies. Using anonymized card transactions and mobile device-based location tracking data, we analyze the factors that contribute to these behavior changes, focusing on stimulus check issuance and state-wide reopening. Our finding suggests that stimulus payment has a significant immediate effect of boosting spending, but it typically does not reverse a downward trend. State-wide reopening had a small effect on spending. Foot traffic increased gradually after stimulus check issuance, but only increased slightly after reopening, which also coincided or preceded several policy changes and confounding events (e.g., protests) in the US. We also find differences in the reaction to these policies in different regions in the US. Our results may be used to inform future economic recovery policies and their potential consumer response.
Weipan Xu, Haohui'Caron' Chen, Enrique Frias-Martinez
et al.
The compact city, as a sustainable concept, is intended to augment the efficiency of urban function. However, previous studies have concentrated more on morphology than on structure. The present study focuses on urban structural elements, i.e., urban hotspots consisting of high-density and high-intensity socioeconomic zones, and explores the economic performance associated with their spatial structure. We use nighttime luminosity (NTL) data and the Loubar method to identify and extract the hotspot and ultimately draw two conclusions. First, with population increasing, the hotspot number scales sublinearly with an exponent of approximately 0.50~0.55, regardless of the location in China, the EU or the US, while the intersect values are totally different, which is mainly due to different economic developmental level. Secondly, we demonstrate that the compactness of hotspots imposes an inverted U-shaped influence on economic growth, which implies that an optimal compactness coefficient does exist. These findings are helpful for urban planning.
We estimated system-wise levelized cost of electricity (LCOE) for a power grid with a high level of renewable energy using our grid optimization model. The estimation results of the system-wise LCOE are discussed in terms of the nexus of energy, environment, and economic growth for Small Island Developing States (SIDS) economies. While 100% renewable energy is technologically possible with the usage of electricity storage, the estimated LCOE is as high as 397 $/MWh which is substantially higher than electricity prices for residential consumers in the US and Japan. The susceptibility analyses imply that the estimated LCOE increase of 223% with a 100% renewable power grid corresponds to an as high as 11% decrease in economic growth. This decrease in economic growth would have a significant negative impact on SIDS economies. However, hydrogen production via the electrolysis of water using the excess energy supply from solar photovoltaics would reduce the LCOE, therefore higher economic growth would be attained with less CO2 emission.
Alfredo Cartone, Geoffrey JD Hewings, Paolo Postiglione
The presence of \b{eta}-convergence in European regions is an important issue to be analyzed. In this paper, we adopt a quantile regression approach in analyzing economic convergence. While previous work has performed quantile regression at the national level, we focus on 187 European NUTS2 regions for the period 1981-2009 and use spatial quantile regression to account for spatial dependence.
This paper is devoted to assess the presence of some regularities in the magnitudes of the earthquakes in Italy between January $24^{th}$, 2016 and January $24^{th}$, 2017, and to propose an earthquakes cost indicator. The considered data includes the catastrophic events in Amatrice and in Marche region. To our purpose, we implement two typologies of rank-size analysis: the classical Zipf-Mandelbrot law and the so-called universal law proposed by Cerqueti and Ausloos (2016). The proposed generic measure of the economic impact of earthquakes moves from the assumption of the existence of a cause-effect relation between earthquakes magnitudes and economic costs. At this aim, we hypothesize that such a relation can be formalized in a functional way to show how infrastructure resistance affects the cost. Results allow us to clarify the impact of an earthquake on the social context and might serve for strengthen the struggle against the dramatic outcomes of such natural phenomena.
The paper proposes a method of financial time series forecasting taking into account the semantics of news. For the semantic analysis of financial news the sampling of negative and positive words in economic sense was formed based on Loughran McDonald Master Dictionary. The sampling included the words with high frequency of occurrence in the news of financial markets. For single-root words it has been left only common part that allows covering few words for one request. Neural networks were chosen for modeling and forecasting. To automate the process of extracting information from the economic news a script was developed in the MATLAB Simulink programming environment, which is based on the generated sampling of positive and negative words. Experimental studies with different architectures of neural networks showed a high adequacy of constructed models and confirmed the feasibility of using information from news feeds to predict the stock prices.
China's new labor law -- Labor Contract Law has been put into practice for over one year. Since its inception, debates have been whirling around the nation, if not the world. In this article, we take an economic perspective to analyze the possible impact of the core item -- open-ended employment contract, and we find that it deals poorly with adverse selection, with moral hazard problems arise, which fails to meet the expectations of law-makers and other parties.
Evangelos Vrettos, Emre C. Kara, Jason MacDonald
et al.
This paper is the second part of a two-part series presenting the results from an experimental demonstration of frequency regulation in a commercial building test facility. In Part I, we developed relevant building models and designed a hierarchical controller for reserve scheduling, building climate control and frequency regulation. In Part II, we introduce the communication architecture and experiment settings, and present extensive experimental results under frequency regulation. More specifically, we compute the day-ahead reserve capacity of the test facility under different assumptions and conditions. Furthermore, we demonstrate the ability of model predictive control to satisfy comfort constraints under frequency regulation, and show that fan speed control can track the fast-moving RegD signal of the Pennsylvania, Jersey, and Maryland Power Market (PJM) very accurately. In addition, we report the observed effects of frequency regulation on building control and provide suggestions for realworld implementation projects. Our results show that hierarchical control is appropriate for frequency regulation from commercial buildings.