
on Network Economics 
By:  Benjamin Golub; Stephen Morris 
Abstract:  In coordination games and speculative overthecounter financial markets, solutions depend on higherorder 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 leastinformed: 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, JeanBaptiste 
Abstract:  We address the problem of finding the optimal lockdown and reopening policy during a pandemic like COVID19 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 tradeoff between the economic cost of the pandemic and the infection incidence allowed by the social planner, and show how this tradeoff 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:  COVID19,healthvswealth 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 freeflow costs (informally their lengths) are within a small multiplicative $(1+\theta)$ constant of the optimal freeflow 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 freeflow 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 pathdisjoint 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 pathdisjoint and even paralleledge networks. In comparison, in the case of $\theta=1$, the PoA in the case of general networks is only $1.6994$, whereas for pathdisjoint/paralleledge 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 (IMFMidle East Center for Economics and Finance (CEF)); Renaud Bourlès (AixMarseille Univ, CNRS, Ecole Centrale, AMSE, Marseille, France); Frédéric Deroïan (AixMarseille Univ, CNRS, AMSE, Marseille, France) 
Abstract:  We analyze risktaking 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 risktaking as a function of the shareholding network. We show that these limits depend on an original centrality measure that relies on the crossshareholding network twice: (i) through a risksharing effect coming from complementarities in risktaking and (ii) through a resource effect that creates heterogeneity among institutions. When risk is large, we find that the risksharing effect relies on a simple centrality measure: the ratio between Bonacich and selfloop centralities. More generally, we show that an increase in crossshareholding increases optimal risktaking through the risksharing effect, but that resource effect can be detrimental to some banks. We show how optimal risktaking levels can be implemented through cash or capital requirements, and analyze complementary interventions through keyplayer analyses. We finally illustrate our model using realworld financial data and discuss extensions toward including debtnetwork, correlated investment portfolios and endogenous networks. 
Keywords:  financial network, risktaking, prudential regulation 
JEL:  C72 D85 
Date:  2020–09 
URL:  http://d.repec.org/n?u=RePEc:aim:wpaimx:2030&r=all 
By:  Raymond KaKay Pang; Oscar Granados; Harsh Chhajer; Erika Fille Legara 
Abstract:  In this work, we investigate the impact of the COVID19 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 COVID19 period decreases, from the peak observed in the 20192020 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 FrancheComté); Eric Kamwa (LC2S, Univ. des Antilles); Muhammad Mahajne (GATE, Univ Lyon) 
Abstract:  In singlewinner elections and individuals expressing linear orderings, an alternative has firstorder 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 firstorder 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 firstorder stochastic dominance rule, which selects the alternative having firstorder stochastic dominance if such an alternative exists, otherwise it selects the alternative which is close to achieve firstorder stochastic dominance. It turns out that this rule is equivalent to the wellstudied Borda rule. This result highlights an unknown property of the Borda rule. 
Keywords:  Network, centrality, centrality measures, rankings, firstorder 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:202005&r=all 