Book of Abstracts

Abstract

1 Cooperative behaviour has been extensively studied, in both evolutionary biology and the social sciences, as a choice between cooperation and defection. However, in many cases, the possibility to not participate or to exit a situation is also available. This type of problem can be studied through the optional public goods game (OPGG). The introduction of the ‘Loner’ strategy, allows players to withdraw from the public goods game, radically changing the dynamics of cooperation in social groups and leading to a never-ending cooperator-defector-loner cycle. While pro-social punishment has been found to help increase cooperation, anti-social punishment - where defectors punish cooperators - causes the downfall of cooperation in both experimental and theoretical studies. In this paper, we extend the theory of the optional public goods game with and Agent-based model, introducing reputational dynamics in the form of social norms that allow agents to condition both their participation and contribution decisions to the reputation of their peers. We benchmark this setup both with respect to the standard optional public goods game and to the variant where all types of punishment are allowed. We find that a social norm imposing a more moderate reputational penalty for opting out than for defecting, increases cooperation. When, besides reputation, punishment is also possible, the two mechanisms work synergically under all norms that do not punish loners too harshly. Under this latter setup, the high levels of cooperation are sustained by conditional strategies, which largely reduce the use of pro-social punishment and almost eliminate anti-social punishment. Our contribution sheds light on the surprising success of reputation in a world under the contemporaneous threat of exploitation and of anti-social punishment. Finally, our results contribute to identifying the conditions that allow e ff ective collective action in the presence of the possibility to opt-out of interactions. Extended abstract 1 We consider a model of a financial market a là Grossmann and Stiglitz, where three types of boundedly rational agents can either trade buying a costly normal signal ✓ on the future return, D + ✓ t + ✏ t ; alternatively, they can trade assuming that some fake news ⇣ t is informative when indeed it’s not, see [1], as ⇣ t ? ✏ t ? ✓ t , 8 t . Agents can choose not to use any signal and stay uninformed. Minimal learning capabilities are introduced in the model: intuitively, every T periods, agents change behaviour when they see that other strategies happen to have produced higher revenues. This copycat learning mechanism is augmented with tiny rates of random mutations. We retrieve some of the findings of the original Grossmann and Stiglitz model and obtain several novel and sharp results. First, we obtain an equilibrium, where the probability to gain more than other strategies is the same as the one of getting less, for all types. We named this peculiar situation, in which no agent has the incentive "in probability" to switch to another type, a "median equilibrium". In the special case with only two types, we provide a semi-analytical expression for the equilibrium fractions. Second, through numerical simulations of an agent-based model, we show that the extinction of misinformed agents is obtained only when T ! 1 and mutation vanishes. In other words, the presence of misinformed traders is pervasive and robust. Third, even when the misinformed agents asymptotically fade away, their decay is extremely slow when T takes low values, i.e., agents (quite) often re-vise their strategy regarding which information to consider (if at all). Hence, trading based on fake news is likely to be observed often. This nicely agrees with the informal observation that agents often are myopic and do not allow themselves a long span of time T to gather data and critically gauge the quality of the available news, see [2]. Extended abstract 1 Computational game theory or algorithmic game theory is a discipline that allows the formal study of the behavior of interacting agents. Unlike practical MAS applications, it is in this formal framework much easier to design behavioral strategies and to provide tools to evaluate them. It is therefore a very useful research area for the MAS community since it allows the design of tools adaptable to practical situations. Since its description by R. Axelrod et W. Hamilton in 1981 [1] the iterated prisoner’s dilemma (IPD) has been the subject of a large number of studies and publications [4]. Particularly, many works and articles about probabilistic strategies for the prisoner’s dilemma have already been realised. In this line of thought, Press & Dyson 2012 ar-ticle [2, 3] has lead to renewed interest in the subject. In this presentation, with the help of a systematic study of probabilistic memory-one strategies, we show that there is a basic criterion to configure and anticipate their success. This criterion, identified through the study of large homogeneous sets of strategies, is then compared to other similar criteria. Our experimental method has allowed us to discover new strategies that are e ffi cient not only in probabilistic environments, but also in more general, probabilistic or non-probabilistic environments [5]. We test the robustness of our results by various methods and compare the new strategies obtained with the best strategies currently known. Extended Abstract 1 In this paper we present an application of the dynamic tracking games framework to a monetary union. We use a small stylized nonlinear two-country macroeconomic model of a monetary union for analysing the interactions between fiscal (governments) and monetary (common central bank) policy makers, assuming different objective functions of these decision makers. Using the OPTGAME algorithm we calculate solutions for two game strategies: one cooperative (Pareto optimal) and one non-cooperative game type (the Nash game for the feedback information pattern). Applying the OPTGAME algorithm to the MUMOD2 model [1], we show how the policy makers react upon demand shocks according to these solution concepts. To this end we introduce two sequences of shocks on the monetary union. The first sequence of shocks aims at describing the dynamics in a monetary union in a situation similar to the economic crisis (2007-2010), the sovereign debt crisis (2010-2013) and the current Covid crisis in Europe. The second sequence of shocks serves to discuss macroeconomic policy strategies for these shocks. In particular, we investigate the welfare consequences of two scenarios: decentralized fiscal policies by independent governments (the present situation), both under a non-cooperative and a cooperative mood of play, and a centralized fiscal policy under different assumptions about the joint objective function corresponding to different weights for the governments in the bargaining process assumed to precede the design of the common fiscal policy. We show the crucial importance of these weights (and hence of the regulations contained in the fiscal constitution of the union) for the results of the outcome in terms of sustainability of fiscal policies and main objective variables of the policy makers. abstract Housing markets are crucial in most economies: the value of the global real estate stock is the highest of any other asset class. This is particularly true for the Italian economy, given their importance for households, banks and the construction sector. Furthermore, as the financial crisis of 2007-2009 has shown, housing and mortgage sectors are critically important for financial stability. In our work we extend and calibrate, with Italian data, the Agent Based Model (ABM) of the real estate and mortgage sectors described in [1]. We do so in order to study the e ff ects of the introduction of borrower-based macroprudential measures. In order to least partially, di ffi design and employ a novel calibration procedure that is built on a multivari-ate moment-based measure and a set of three search algorithms: the low discrepancy series; a metamodel built using a random forest a With the calibrated and validated model we evaluate the e ff ects of three hypothetical macroprudential policies, applicable to newly issued mortgages: an 80% loan-to-value cap; a 30% cap on the loan service to income ratio; a combination of both policies. We find that these policy interventions tend to slow-down the credit and housing cycles and reduce the probability of de-faults on mortgages. However, these e ff ects are very small over a five years horizon. This result is consistent with the view that the Italian household sector is already financially sound. Finally, we find that restrictive policies induce a shift in demand toward lower quality dwellings. Due to household heterogeneity, this e ff ect is stronger for market segments with a higher concentration of constrained households. Extended abstract 1 Organizations have often been studied unidimensionally: Researchers have focused on either the individual, the team or the organizational level [1]. Consequently, research that considers how the di ff erent levels interact is not extensive. We aim to contribute to the literature by employing a multidimensional approach that considers two di ff erent levels: The individual and the team. Following [2], we implement a multilevel design that considers the emergence of macro-level e ff ects coming from micro-level behaviour to address a problem with practical relevance. Our focus lies in complex tasks solved by groups of human decision-makers, and we study how the interactions between individual learning and group adaptation a ff ect task performance. We implement an agent-based model based on the NK-framework [3]. In this setting, a population of agents with heterogeneous capabilities is modeled. These heterogeneous capabilities imply that agents di ff er in (i) the subtask they can solve and (ii) the solutions to t