arXiv subjects

Cesare Carissimo

Publications and source records attributed to Cesare Carissimo.

8 recordsLinked to original sources

arXiv2026

The Many-Body Problem of the Data Centre

Modern Artificial Intelligence is often framed as limited by its own disembodiment, as if giving it a body would unlock its true potential. We argue to the contrary that it is the Data Centre that is, in many cases, the body of the AI. At the same time, the Data Centre is part of the labouring body of Capital and possesses staggering organismic qualities when seen through a biological lens. We elucidate the organic analogy and identify the many-body problem that stems from the Data Centre being a non-unique, universal form of embodiment. We identify the intimate connection between computation and human desires in how the Data Centre archives, serves, and computes on data born to the desires of humans. Strikingly, while the Data Centre echoes the ghosts of human desires, it acts without desire of its own. The organismic analogy begins to split at its seams, but Capital does not care. Automata and human labour are priced into the market much the same. We argue that through the pricing of artificial intelligence Capital distils most clearly the value of intelligence and allows for its comparison across the organism - mechanism divide.

cs.AI↗

arXiv2025

Does Capital Dream of Artificial Labour?

This paper investigates the concept of Labour as an expression of `timenergy' - a fusion of time and energy - and its entanglement within the system of Capital. We define Labour as the commodified, quantifiable expansion of timenergy, in contrast to Capital, which is capable of accumulation and abstraction. We explore Labour's historical evolution, its coercive and alienating nature, and its transformation through automation and artificial intelligence. Using a game-theoretic, agent-based simulation, we model interactions between Capital and Labour in production processes governed by Cobb-Douglas functions. Our results show that despite theoretical symmetry, learning agents disproportionately gravitate toward capital-intensive processes, revealing Capital's superior organizational influence due to its accumulative capacity. We argue that Capital functions as an artificially alive system animated by the living Labour it consumes, and question whether life can sustain itself without the infrastructures of Capital in a future of increasing automation. This study offers both a critique of and a framework for understanding Labour's subjugation within the Capital system.

cs.CY↗

arXiv2025

Algorithmic Collusion is Algorithm Orchestration

We propose a fresh `meta-game' perspective on the problem of algorithmic collusion in pricing games a la Bertrand. Economists have interpreted the fact that algorithms can learn to price collusively as tacit collusion. We argue instead that the co-parametrization of algorithms, in ways as are necessary to obtain algorithmic collusion, typically requires algorithm designers to engage in some form of explicit collusion or `algorithm orchestration.' In our model, the algorithm designers play a meta-game of parametrizing their algorithms, which then play repeated Bertrand competition. The strategic analysis at the meta-level reveals new equilibrium and collusion phenomena. (JEL: C62, C63, D43, L13)

econ.TH↗

arXiv2025

Cycles and collusion in congestion games under Q-learning

We investigate the dynamics of Q-learning in a class of generalized Braess paradox games. These games represent an important class of network routing games where the associated stage-game Nash equilibria do not constitute social optima. We provide a full convergence analysis of Q-learning with varying parameters and learning rates. A wide range of phenomena emerges, broadly either settling into Nash or cycling continuously in ways reminiscent of "Edgeworth cycles" (i.e. jumping suddenly from Nash toward social optimum and then deteriorating gradually back to Nash). Our results reveal an important incentive incompatibility when thinking in terms of a meta-game being played by the designers of the individual Q-learners who set their agents' parameters. Indeed, Nash equilibria of the meta-game are characterized by heterogeneous parameters, and resulting outcomes achieve little to no cooperation beyond Nash. In conclusion, we suggest a novel perspective for thinking about regulation and collusion, and discuss the implications of our results for Bertrand oligopoly pricing games.

econ.TH↗

arXiv2025

Overcoming the Price of Anarchy by Steering with Recommendations

Varied real world systems such as transportation networks, supply chains and energy grids present coordination problems where many agents must learn to share resources. It is well known that the independent and selfish interactions of agents in these systems may lead to inefficiencies, often referred to as the `Price of Anarchy'. Effective interventions that reduce the Price of Anarchy while preserving individual autonomy are of great interest. In this paper we explore recommender systems as one such intervention mechanism. We start with the Braess Paradox, a congestion game model of a routing problem related to traffic on roads, packets on the internet, and electricity on power grids. Following recent literature, we model the interactions of agents as a repeated game between $Q$-learners, a common type of reinforcement learning agents. This work introduces the Learning Dynamic Manipulation Problem, where an external recommender system can strategically trigger behavior by picking the states observed by $Q$-learners during learning. Our computational contribution demonstrates that appropriately chosen recommendations can robustly steer the system towards convergence to the social optimum, even for many players. Our theoretical and empirical results highlight that increases in the recommendation space can increase the steering potential of a recommender system, which should be considered in the design of recommender systems.

cs.GT↗

arXiv2024

Capital as Artificial Intelligence

We gather many perspectives on Capital and synthesize their commonalities. We provide a characterization of Capital as a historical agential system and propose a model of Capital using tools from computer science. Our model consists of propositions which, if satisfied by a specific grounding, constitute a valid model of Capital. We clarify the manners in which Capital can evolve. We claim that, when its evolution is driven by quantitative optimization processes, Capital can possess qualities of Artificial Intelligence. We find that Capital may not uniquely represent meaning, in the same way that optimization is not intentionally meaningful. We find that Artificial Intelligences like modern day Large Language Models are a part of Capital. We link our readers to a web-interface where they can interact with a part of Capital.

cs.CY↗

arXiv2023

Dynamic value alignment through preference aggregation of multiple objectives

The development of ethical AI systems is currently geared toward setting objective functions that align with human objectives. However, finding such functions remains a research challenge, while in RL, setting rewards by hand is a fairly standard approach. We present a methodology for dynamic value alignment, where the values that are to be aligned with are dynamically changing, using a multiple-objective approach. We apply this approach to extend Deep $Q$-Learning to accommodate multiple objectives and evaluate this method on a simplified two-leg intersection controlled by a switching agent.Our approach dynamically accommodates the preferences of drivers on the system and achieves better overall performance across three metrics (speeds, stops, and waits) while integrating objectives that have competing or conflicting actions.

cs.AI↗

arXiv2023

Pushdown Automata and Context-Free Grammars in Bisimulation Semantics

The Turing machine models an old-fashioned computer, that does not interact with the user or with other computers, and only does batch processing. Therefore, we came up with a Reactive Turing Machine that does not have these shortcomings. In the Reactive Turing Machine, transitions have labels to give a notion of interactivity. In the resulting process graph, we use bisimilarity instead of language equivalence. Subsequently, we considered other classical theorems and notions from automata theory and formal languages theory. In this paper, we consider the classical theorem of the correspondence between pushdown automata and context-free grammars. By changing the process operator of sequential composition to a sequencing operator with intermediate acceptance, we get a better correspondence in our setting. We find that the missing ingredient to recover the full correspondence is the addition of a notion of state awareness.

cs.LO↗