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2020 / Drews, S., F. Exadaktylos and J. van den Bergh

Assessing synergy of incentives and nudges in the energy policy mix

Energy Policy, Vol. 144, Art.-Nr. 111605
Should policy-makers combine price incentives with behavioural nudges to encourage sustainable energy behaviour? Available evidence from various behavioural sciences is scarce and inconclusive about synergy of the two instruments. This is partly due to methodological limitations. We offer a framework to overcome such limitations in future research and to guide policy-making. It includes four cases: no synergy, positive synergy, weak negative synergy, and strong negative synergy or backfire. The adoption of a policy mix is recommended in the first two cases, and may be pursued in the third case. To clarify the underlying mechanisms of the synergy, a distinction is made between crowding (in/out) of intrinsic motivations by incentives and crowding (in/out) of extrinsic motivations by nudges. This distinction turns out to be especially relevant in the case of weakly negative synergy, as here behavioural and temporal spillover effects require consideration from the policy-maker as well. We end with broader reflections regarding other policy criteria for the design of an adequate energy policy mix.
2020 / Grabisch, M. and F. Li

Anti-conformism in the threshold model of collective behavior

Dynamic Games and Applications, Vol. 10(2), 444-477
We provide a detailed study of the threshold model, where both conformist and anti-conformist agents coexist. Our study bears essentially on the convergence of the opinion dynamics in the society of agents, i.e., finding absorbing classes, cycles, etc. Also, we are interested in the existence of cascade effects, as this may constitute an undesirable phenomenon in collective behavior. We divide our study into two parts. In the first one, we basically study the threshold model supposing a fixed complete network, where every one is connected to every one, like in the seminal work of Granovetter. We study the case of a uniform distribution of the threshold, of a Gaussian distribution, and finally give a result for arbitrary distributions, supposing there is one type of anti-conformist. In a second part, we suppose that the neighborhood of an agent is random, drawn at each time step from a distribution. We distinguish the case where the degree (number of links) of an agent is fixed, and where there is an arbitrary degree distribution. We show the existence of cascades and that for most societies, the opinion converges to a chaotic situation.
2020 / Rengs, B., M. Scholz-Wäckerle and J. van den Bergh

Evolutionary macroeconomic assessment of employment and innovation impacts of climate policy packages

Journal of Economic Behavior and Organization, 169, 332-368
Climate policy has been mainly studied with economic models that assume representative, rational agents. Such policy aims, though, at changing carbon-intensive consumption and production patterns driven by bounded rationality and other-regarding preferences, such as status and imitation. To examine climate policy under such alternative behavioral assumptions, we develop a model tool by adapting an existing general-purpose macroeconomic multi-agent model. The resulting tool allows testing various climate policies in terms of combined climate and economic performance. The model is particularly suitable to address the distributional impacts of climate policies, not only because populations of many agents are included, but also as these are composed of different classes of households. The approach accounts for two types of innovations, which improve either the carbon or labor intensity of production. We simulate policy scenarios with distinct combinations of carbon taxation, a reduction of labor taxes, subsidies for green innovation, a price subsidy to consumers for less carbon-intensive products, and green government procurement. The results show pronounced differences with those obtained by rational-agent model studies. It turns out that a supply-oriented subsidy for green innovation, funded by the revenues of a carbon tax, results in a significant reduction of carbon emissions without causing negative effects on employment. On the contrary, demand-oriented subsidies for adopting greener technologies, funded in the same manner, result in either none or considerably less reduction of carbon emissions and may even lead to higher unemployment. Our study also contributes insight on a potential double dividend of shifting taxes from labor to carbon.
2020 / Van den Brink, R. and A. Rusinowska

The degree ratio ranking method for directed graphs

European Journal of Operational Research, Vol. 288(2), 563-575
One of the most famous ranking methods for digraphs is the ranking by Copeland score. The Copeland score of a node in a digraph is the difference between its outdegree (i.e. its number of outgoing arcs) and its indegree (i.e. its number of ingoing arcs). In the ranking by Copeland score, a node is ranked higher, the higher is its Copeland score. In this paper, we deal with an alternative method to rank nodes according to their out- and indegree, namely ranking the nodes according to their degree ratio, i.e. the outdegree divided by the indegree. To avoid dividing by zero, we add 1 to both the out- as well as indegree of every node. We provide an axiomatization of the ranking by degree ratio using a clone property, which says that the entrance of a clone or a copy (i.e. a node that is in some sense similar to the original node) does not change the ranking among the original nodes. We also provide a new axiomatization of the ranking by Copeland score using the same axioms except that this method satisfies a different clone property. Finally, we modify the ranking by degree ratio by taking only the out- and indegree, but by definition assume nodes with indegree zero to be ranked higher than nodes with positive indegree. We provide an axiomatization of this ranking method using yet another clone property and a maximal property. In this way, we can compare the three ranking methods by their clone property.
2019 / Konc T. and I. Savin

Social reinforcement with weighted interactions

Physical Review, E 100, 022305
The speed and extent of diffusion of behaviors in social networks depends on network structure and individual preferences. The contribution of the present study is twofold. First, we introduce weighted interactions between potential adopters that depend on the similarity in their preferences and moderate the strength of social reinforcement. The reason for the extension is the existence of a confirmation bias in the way agents treat information by prioritizing evidence conforming to their opinion. As a result, individuals become less likely to be influenced by peers with relatively different preferences, reducing the overall diffusion rate under clustered networks. Second, we enrich our analysis by also considering a scale free network topology with a high degree asymmetry, motivated by its pervasiveness in online social networks. This network performs consistently well in terms of diffusion for different parameter combinations and clearly outperforms clustered networks under weighted interactions. Our results show that more realistic assumptions regarding agents' interactions shift the focus from clustering to degree distribution in the study of network structures allowing for fast and widespread behavior adoption.
2019 / Hibbah, E.H., H. El Maroufy, C. Fuchs and T. Ziad

An MCMC computational approach for a continuous time state-dependent regime switching diffusion process

JOURNAL OF APPLIED STATISTICS, Vol. 47, Issue 8, 1354-1374
State-dependent regime switching diffusion processes or hybrid switching diffusion (HSD) processes are hard to simulate with classical methods which leads us to adopt a Markov chain Monte Carlo (MCMC) Bayesian approach very convenient to estimate complicated models such as the HSD one. In the HSD, the diffusion component is dependent on the switching discrete hidden regimes and the transition rates of the regime switching are dependent on the diffusion observations. Since in reality phenomena are only observed in discrete times, data imputation is called for to create more observations so as to have good approximations for the density of the diffusion process. Three categories of entities will be computed in a Bayesian context: The latent imputed observations, the regime switching states, and the parameters of the models. The latent imputed data is updated at random time intervals in block using a Metropolis Hastings algorithm. The switching states are computed by an adaptation of a forward filtering backward smoothing algorithm to the HSD model. The parameters are estimated after prior specifications and conditional posterior densities formulation using Gibbs sampler or Metropolis Hastings algorithm.
2019 / Cornea-Madeira, A., C. Hommes and D. Massaro

Behavioral Heterogeneity in U.S. Inflation Dynamics

Journal of Business & Economic Statistics, Vol. 37(2), 288-300
In this article we develop and estimate a behavioral model of inflation dynamics with heterogeneous firms. In our stylized framework there are two groups of price setters, fundamentalists and random walk believers. Fundamentalists are forward-looking in the sense that they believe in a present-value relationship between inflation and real marginal costs, while random walk believers are backward-looking, using the simplest rule of thumb, naive expectations, to forecast inflation. Agents are allowed to switch between these different forecasting strategies conditional on their recent relative forecasting performance. We estimate the switching model using aggregate and survey data. Our results support behavioral heterogeneity and the significance of evolutionary learning mechanism. We show that there is substantial time variation in the weights of forward-looking and backward-looking behavior. Although on average the majority of firms use the simple backward-looking rule, the market has phases in which it is dominated by either the fundamentalists or the random walk believers.
2019 / Liuzzi, D., P. Pellizzari and M. Tolotti

Fast traders and slow price adjustments: an artificial market with strategic interaction and transaction costs

Journal of Economic Interaction and Coordination, Vol. 14, 643–662
In this paper, we propose an artificial market to model high-frequency trading where fast traders use threshold rules strategically to issue orders based on a signal reflecting the level of stochastic liquidity prevailing on the market. A market maker is in charge of adjusting prices (on a fast scale) and of setting closing prices and transaction costs on a daily basis, controlling for the volatility of returns and market activity. We first show that a baseline version of the model with no frictions is able to generate returns endowed with several stylized facts. This achievement suggests that the two time scales used in the model are one (possibly novel) way to obtain realistic market outcomes and that high-frequency trading can amplify liquidity shocks. We then explore whether transaction costs can be used to control excess volatility and improve market quality. While properly implemented taxation schemes may help in reducing volatility, care is needed to avoid excessively curbing activity in the market and intensifying the occurrence of abnormal peaks in returns.
2019 / Hommes, C. and J. Lustenhouwer

Inflation targeting and liquidity traps under endogenous credibility

Journal of Monetary Economics, Vol. 107, 48-62
Policy implications are derived for an inflation-targeting central bank, whose credibility is endogenous and depends on its past ability to achieve its targets. This is done in a New Keynesian framework with heterogeneous and boundedly rational expectations. We find that the region of allowed policy parameters is strictly larger than under rational expectations. However, when the zero lower bound on the nominal interest rate is accounted for, self-fulfilling deflationary spirals can occur, depending on the credibility of the central bank. Deflationary spirals can be prevented with a high inflation target and aggressive monetary easing.
2019 / El Omari M., H. El Maroufy and C. Fuchs

Non parametric estimation for fractional diffusion processes with random effects

STATISTICS, Vol. 53 (4), 753-769
We propose a nonparametric estimation for a class of fractional stochastic differential equations (FSDE) with random effects. We precisely consider general linear fractional stochastic differential equations with drift depending on random effects and non-random diffusion. We build ordinary kernel estimators and histogram estimators and study their risk (), when . Asymptotic results are evaluated as both and N tend to infinity.
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