Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Selfishly Prepaying in Financial Credit Networks
Authors: Hao Zhou, Yongzhao Wang, Konstantinos Varsos, Nicholas Bishop, Rahul Savani, Anisoara Calinescu, Michael Wooldridge
JAIR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This study investigates prepayments from both theoretical and empirical perspectives. We first establish the computational complexity of finding prepayments that maximize welfare, assuming global coordination among firms in the financial network. Subsequently, our focus shifts to understanding the strategic behavior of individual firms in the presence of prepayments. We introduce a prepayment game where firms strategically make prepayments, delineating the existence of pure strategy Nash equilibria and analyzing the price of anarchy (stability) within this game. Recognizing the computational challenges associated with determining Nash equilibria in prepayment games, we use a simulation-based approach, known as empirical game-theoretic analysis (EGTA). Through EGTA, we are able to find Nash equilibria among a carefully-chosen set of heuristic strategies. By examining the equilibrium behavior of firms, we outline the characteristics of high-performing strategies for strategic prepayments and establish connections between our empirical and theoretical findings. |
| Researcher Affiliation | Academia | Hao Zhou EMAIL Department of Computer Science, University of Oxford, UK Yongzhao Wang EMAIL Konstantinos Varsos EMAIL The Alan Turing Institute, UK Nicholas Bishop EMAIL Department of Computer Science, University of Oxford, UK Rahul Savani EMAIL Department of Computer Science, University of Liverpool, UK The Alan Turing Institute, UK Anisoara Calinescu EMAIL Michael Wooldridge EMAIL Department of Computer Science, University of Oxford, UK |
| Pseudocode | No | The paper describes heuristic strategies in text format, for example: 'No Operation (h1): Make no prepayment.', but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code and data used for the simulations and experiments of this paper are publicly available at https://github.com/haozhou-egta/prepayment-game. |
| Open Datasets | No | Our empirical analysis of prepayments on synthetic networks. Given the computational challenges in determining NE in prepayment games involving multiple firms, as indicated in Theorem 6, we employ EGTA, a process that engages in game-theoretic reasoning through extensive simulation. Our approach begins by proposing a set of heuristic prepayment strategies, which, in contrast to the theoretical results in Section 4, rely solely on local information that would definitely be available to firms, such as claims (expected incoming payments), liabilities, and external assets. We study prepayment games under various configurations of credit-network generators. A network generator creates a credit-network instance by sampling a configuration from the distribution prescribed in Table 3, yielding various network typologies, different scales of external assets (i.e., uniform distributions over scale intervals), and liabilities. |
| Dataset Splits | No | With each utility function, a pure-strategy profile in the reduced game will be evaluated by averaging the payoffs over 1000 instances sampled from the generator. This means data is generated on the fly for each evaluation rather than using predefined splits of a static dataset. |
| Hardware Specification | No | The paper does not specify any particular hardware used for running the simulations or experiments. |
| Software Dependencies | No | The paper mentions 'empirical game-theoretic analysis (EGTA)' and 'replicator dynamics (RD)' as methods but does not provide specific software names with version numbers used for implementation or analysis. |
| Experiment Setup | Yes | We study prepayment games under various configurations of credit-network generators. A network generator creates a credit-network instance by sampling a configuration from the distribution prescribed in Table 3, yielding various network typologies, different scales of external assets (i.e., uniform distributions over scale intervals), and liabilities. We also sample a subset of firms with shocks, assigned by the lower bound of the external-asset distribution as their external assets. Shocks model situations where the financial condition of a firm is poor. In this study, we simplify prepayment games with N = 10 firms and 9 strategies to games with k = 4 firms and 9 strategies. With each utility function, a pure-strategy profile in the reduced game will be evaluated by averaging the payoffs over 1000 instances sampled from the generator. Table 3: Prepayment game parameters. Parameters Values Number of Firms 10 Number of Edges U(0, 9) for each firm Number of Strategies 9 for each firm Utility Function {Total Asset, Equity} External Assets {U(0, 40), U(40, 70), U(70, 120)} Liabilities U(0, x), for x {10, 20, 35} Shocks U(0, 5). |