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on Network Economics |
| By: | Benjamin Golub; Stephen Morris |
| Abstract: | In coordination games and speculative over-the-counter financial markets, solutions depend on higher-order average expectations: agents' expectations about what counterparties, on average, expect their counterparties to think, etc. We offer a unified analysis of these objects and their limits, for general information structures, priors, and networks of counterparty relationships. Our key device is an interaction structure combining the network and agents' beliefs, which we analyze using Markov methods. This device allows us to nest classical beauty contests and network games within one model and unify their results. Two applications illustrate the techniques: The first characterizes when slight optimism about counterparties' average expectations leads to contagion of optimism and extreme asset prices. The second describes the tyranny of the least-informed: agents coordinating on the prior expectations of the one with the worst private information, despite all having nearly common certainty, based on precise private signals, of the ex post optimal action. |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2009.13802&r=all |
| By: | Pongou, Roland; Tchuente, Guy; Tondji, Jean-Baptiste |
| Abstract: | We address the problem of finding the optimal lockdown and reopening policy during a pandemic like COVID-19 for a social planner who prioritizes health over the economy. Agents are connected through a fuzzy network of contacts, and the planner's objective is to determine the policy that contains the spread of infection below a tolerable incidence level, and that maximizes the present discounted value of real income, in that order of priority. We show theoretically that the planner's problem has a unique solution. The optimal policy depends both on the configuration of the contact network and the tolerated infection incidence. Using simulations, we apply these theoretical findings to: (i) quantify the trade-off between the economic cost of the pandemic and the infection incidence allowed by the social planner, and show how this trade-off depends on network configuration; (ii) understand the correlation between different measures of network centrality and individual lockdown probability, and derive implications for the optimal design of surveys on social distancing behavior and network structure; and (iii) analyze how segregation induces differential health and economic dynamics in minority and majority populations, also illustrating the crucial role of patient zero in these dynamics. |
| Keywords: | COVID-19,health-vs-wealth prioritization,economic cost,fuzzy networks,network centrality,segregation,patient zero,optimally targeted lockdown policy |
| JEL: | E61 H12 I18 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:glodps:667&r=all |
| By: | Francisco Benita; Vittorio Bil\`o; Barnab\'e Monnot; Georgios Piliouras; Cosimo Vinci |
| Abstract: | We investigate traffic routing both from the perspective of real world data as well as theory. First, we reveal through data analytics a natural but previously uncaptured regularity of real world routing behavior. Agents only consider, in their strategy sets, paths whose free-flow costs (informally their lengths) are within a small multiplicative $(1+\theta)$ constant of the optimal free-flow cost path connecting their source and destination where $\theta\geq0$. In the case of Singapore, $\theta=1$ is a good estimate of agents' route (pre)selection mechanism. In contrast, in Pigou networks the ratio of the free-flow costs of the routes and thus $\theta$ is infinite, so although such worst case networks are mathematically simple they correspond to artificial routing scenarios with little resemblance to real world conditions, opening the possibility of proving much stronger Price of Anarchy guarantees by explicitly studying their dependency on $\theta$. We provide an exhaustive analysis of this question by providing provably tight bounds on PoA($\theta$) for arbitrary classes of cost functions both in the case of general congestion/routing games as well as in the special case of path-disjoint networks. For example, in the case of the standard Bureau of Public Roads (BPR) cost model, $c_e(x)= a_e x^4+b_e$ and more generally quartic cost functions, the standard PoA bound for $\theta=\infty$ is $2.1505$ (Roughgarden, 2003) and it is tight both for general networks as well as path-disjoint and even parallel-edge networks. In comparison, in the case of $\theta=1$, the PoA in the case of general networks is only $1.6994$, whereas for path-disjoint/parallel-edge networks is even smaller ($1.3652$), showing that both the route geometries as captured by the parameter $\theta$ as well as the network topology have significant effects on PoA (Figure 1). |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2009.12871&r=all |
| By: | Mohamed Belhaj (IMF-Midle East Center for Economics and Finance (CEF)); Renaud Bourlès (Aix-Marseille Univ, CNRS, Ecole Centrale, AMSE, Marseille, France); Frédéric Deroïan (Aix-Marseille Univ, CNRS, AMSE, Marseille, France) |
| Abstract: | We analyze risk-taking regulation when financial institutions are linked through shareholdings. We model regulation as an upper bound on institutions' default probability, and pin down the corresponding limits on risk-taking as a function of the shareholding network. We show that these limits depend on an original centrality measure that relies on the cross-shareholding network twice: (i) through a risk-sharing effect coming from complementarities in risk-taking and (ii) through a resource effect that creates heterogeneity among institutions. When risk is large, we find that the risk-sharing effect relies on a simple centrality measure: the ratio between Bonacich and self-loop centralities. More generally, we show that an increase in cross-shareholding increases optimal risk-taking through the risk-sharing effect, but that resource effect can be detrimental to some banks. We show how optimal risk-taking levels can be implemented through cash or capital requirements, and analyze complementary interventions through key-player analyses. We finally illustrate our model using real-world financial data and discuss extensions toward including debt-network, correlated investment portfolios and endogenous networks. |
| Keywords: | financial network, risk-taking, prudential regulation |
| JEL: | C72 D85 |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:aim:wpaimx:2030&r=all |
| By: | Raymond Ka-Kay Pang; Oscar Granados; Harsh Chhajer; Erika Fille Legara |
| Abstract: | In this work, we investigate the impact of the COVID-19 pandemic on sovereign bond yields amongst European countries. We consider the temporal changes from financial correlations using network filtering methods. These methods consider a subset of links within the correlation matrix, which gives rise to a network structure. We use sovereign bond yield data from 17 European countries between the 2010 and 2020 period as an indicator of the economic health of countries. We find that the average correlation between sovereign bonds within the COVID-19 period decreases, from the peak observed in the 2019-2020 period, where this trend is also reflected in all network filtering methods. We also find variations between the movements of different network filtering methods under various network measures. |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2009.13390&r=all |
| By: | Mostapha Diss (CRESE, Univ. Bourgogne Franche-Comté); Eric Kamwa (LC2S, Univ. des Antilles); Muhammad Mahajne (GATE, Univ Lyon) |
| Abstract: | In single-winner elections and individuals expressing linear orderings, an alternative has first-order stochastic dominance if the cumulative standing for this alternative at each rank is higher than that of the other alternatives. It is well known that this criterion may fail in ranking the competing alternatives since the first-order stochastic dominance winner may not exist in some situations. Making an adaptation of a centrality measure from network theory, we introduce in this note a rule, called the almost first-order stochastic dominance rule, which selects the alternative having first-order stochastic dominance if such an alternative exists, otherwise it selects the alternative which is close to achieve first-order stochastic dominance. It turns out that this rule is equivalent to the well-studied Borda rule. This result highlights an unknown property of the Borda rule. |
| Keywords: | Network, centrality, centrality measures, rankings, first-order stochastic dominance, scoring rules, Borda’s rule. |
| JEL: | C71 D71 D72 D85 |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:crb:wpaper:2020-05&r=all |