arXiv subjects

Soumen Banerjee

Publications and source records attributed to Soumen Banerjee.

5 recordsLinked to original sources

arXiv2026

The Reverse Big Push: Generative AI and Self-Fulfilling Automation

Generative AI relocates the fixed cost of automation. A model provider pays to train a frontier system, while a downstream firm rents capability by usage; the same firm must carry a continuing payroll to supply a human-augmented service. We study this asymmetry in a local service economy with household budgets and a market-clearing wage. Human augmentation earns a larger surplus from an additional customer, whereas automation has the lower break-even scale. Payroll supports demand across sectors. Wage adjustment works against this feedback but does not generally undo it: when the wage-income effect dominates the fall in the wage bill per retained worker, production modes are strategic complements. The economy can then possess both a high-demand human-augmented equilibrium and a low-demand automated equilibrium. The former is the local first best, even when flexible wages prevent a firm-profit ranking. With forward-looking firms and staggered revision opportunities, the same inherited employment structure can support an automation cascade or an augmentation recovery; the anticipated path of later adopters validates the first movers' choices. Under the regularity and boundary conditions of Frankel and Pauzner, a public aggregate shock selects a unique state-contingent path, while the vanishing-friction limit selects according to risk dominance. A transparent parameterization anchored to professional-services revenue-to-payroll ratios illustrates how high-autonomy uses can enter the coordination region and how wage adjustment compresses that region. Optimal policy combines the adoption wedge created by demand spillovers with a temporary bridge when the low state is locally self-sustaining.

econ.TH↗

arXiv2025

Correlated equilibrium implementation: Navigating toward social optima with learning dynamics

Implementation theory has made significant advances in characterizing which social choice functions can be implemented in Nash equilibrium, but these results typically assume sophisticated strategic reasoning by agents. However, evidence exists to show that agents frequently cannot perform such reasoning. In this paper, we present a finite mechanism which fully implements Maskin-monotonic social choice functions as the outcome of the unique correlated equilibrium of the induced game. Due to the results in Hart and MasColell (2000), this yields that even when agents use a simple adaptive heuristic like regret minimization rather than computing equilibrium strategies, the designer can expect to implement the SCF correctly. We demonstrate the mechanism's effectiveness through simulations in a bilateral trade environment, where agents using regret matching converge to the desired outcomes despite having no knowledge of others' preferences or the equilibrium structure. The mechanism does not use integer games or modulo games.

econ.TH↗

arXiv2023

Interpreting and Countering Collusion in Deep-Learning Pricing Algorithms

Algorithmic pricing raises a question of interpretation as well as intervention: when autonomous deep-learning pricing systems sustain supracompetitive prices, what strategic pattern have they learned, and how might market institutions alter it? This paper develops an interpretable framework for studying learned collusion in repeated pricing environments. The framework embeds strategic deep learning networks in a differentiated-products Bertrand market and compresses recent price histories into finite states that record price levels, rival price movements, and movement persistence. This state representation preserves the dynamic information relevant for reward and punishment while making learned behavior economically interpretable. In the baseline environment, agents learn supracompetitive prices and exhibit a coherent collusive asymmetry: they punish rival price cuts and accommodate rival price increases. The paper then uses this framework to study an order-book mechanism that assembles temporary buyer commitments and allocates them to sellers willing to make sufficiently deep undercuts, partially insulating those undercutters from retaliatory punishment. The mechanism lowers realized prices in the main symmetric-cost design and remains effective in the main robustness exercises. Further analysis shows that this price reduction operates through the intended channel: qualifying undercuts become less exposed to subsequent punishment, reducing the continuation loss that sustains high-price states. The results show how interpretable learning frameworks can connect algorithmic pricing outcomes to economic mechanisms, and how market design can target the enforcement channel behind learned collusion.

econ.TH↗

arXiv2022

Implementation with Uncertain Evidence

We study a full implementation problem with a state unknown to the designer but known to agents, where agents have uncertain evidence privately drawn from state-dependent distributions. Stochastic evidence enables ``perfect deceptions,'' where agents' reports can mimic the evidence distribution of a false state, making differentiation impossible for any mechanism. This yields our main result: a necessary and sufficient condition, No Perfect Deceptions (NPD), for implementation in (mixed-strategy) Bayesian Nash equilibria. The solution requires novel techniques like belief elicitation via competing scoring rules, and an endogenous ``test allocation'' using the evidence structure. For informationally small agents (McLean and Postlewaite (2002)), a generalized condition (GNPD) is sufficient. Our mechanisms work for two or more agents, avoid integer/modulo games, and use limited liability transfers that vanish in equilibrium.

econ.TH↗

arXiv2021

Direct Implementation with Evidence

We study full implementation with evidence in an environment with bounded utilities. We show that a social choice function is Nash implementable in a direct revelation mechanism if and only if it satisfies the measurability condition proposed by BL2012 . Building on a novel classification of lies according to their refutability with evidence, the mechanism requires only two agents, accounts for mixed-strategy equilibria and accommodates evidentiary costs. While monetary transfers are used, they are off the equilibrium and can be balanced with three or more agents. In a richer model of evidence due to KT2012 , we establish pure-strategy implementation with two or more agents in a direct revelation mechanism. We also obtain a necessary and sufficient condition on the evidence structure for renegotiation-proof bilateral contracts, based on the classification of lies.

econ.TH↗