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3721 items in total found

Journal Articles | 2017

Investigating a An evolutionary analysis of growth and fluctuations with negative externalities

Anindya S. Chakrabarti and Ratul Lakhar

Dynamic Games and Applications

We present an evolutionary game theoretic model of growth and fluctuations with negative externalities. Agents in a population choose the level of input. Total output is a function of aggregate input and a productivity parameter. The model, which is equivalent to a tragedy of the commons, constitutes an aggregative potential game with negative externalities. Aggregate input at the Nash equilibrium is inefficiently high causing aggregate payoff to be suboptimally low. Simulations with the logit dynamic reveal that while the aggregate input increases monotonically from an initial low level, aggregate payoff may decline from the corresponding high level. Hence, a positive technology shock causes a rapid initial increase in aggregate payoff, which is unsustainable as agents increase aggregate input to the inefficient equilibrium level. Aggregate payoff, therefore, declines subsequently. A sequence of exogenous shocks, therefore, generates a sustained pattern of growth and fluctuations in aggregate payoff.

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Journal Articles | 2017

Emergence of anti-coordination through reinforcement learning in generalized minority games

Anindya S. Chakrabarti and Diptesh Ghosh

Journal of Economic Interaction and Coordination

In this paper we propose adaptive strategies to solve coordination failures in a prototype generalized minority game model with a multi-agent, multi-choice environment. We illustrate the model with an application to large scale distributed processing systems with a large number of agents and servers. In our set up, agents are assigned responsibility to complete tasks that require unit time. They request servers to process these tasks. Servers can process only one task at a time. Agents have to choose servers independently and simultaneously, and have access to the outcomes of their own past requests only. Coordination failure occurs if more than one agent simultaneously requests the same server to process tasks at the same time, while other servers remain idle. Since agents are independent, this leads to multiple coordination failures. In this paper, we propose strategies based on reinforcement learning that minimize such coordination failures. We also prove a null result that a large category of probabilistic strategies which attempts to combine information about other agents’ strategies, asymptotically converge to uniformly random choices over the servers.

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Journal Articles | 2017

Financial fluctuations anchored to economic fundamentals: A mesoscopic network approach

Kiran Sharma, Balagopal Gopalakrishnan, Anindya S. Chakrabarti, and Anirban Chakraborti

Scientific Reports

We demonstrate the existence of an empirical linkage between nominal financial networks and the underlying economic fundamentals, across countries. We construct the nominal return correlation networks from daily data to encapsulate sector-level dynamics and infer the relative importance of the sectors in the nominal network through measures of centrality and clustering algorithms. Eigenvector centrality robustly identifies the backbone of the minimum spanning tree defined on the return networks as well as the primary cluster in the multidimensional scaling map. We show that the sectors that are relatively large in size, defined with three metrics, viz., market capitalization, revenue and number of employees, constitute the core of the return networks, whereas the periphery is mostly populated by relatively smaller sectors. Therefore, sector-level nominal return dynamics are anchored to the real size effect, which ultimately shapes the optimal portfolios for risk management. Our results are reasonably robust across 27 countries of varying degrees of prosperity and across periods of market turbulence (2008–09) as well as periods of relative calmness (2012–13 and 2015–16).

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Journal Articles | 2017

Quantifying invariant features of within-group inequality in consumption across groups.

Anindya S. Chakrabarti, Arnab Chatterjee, Tushar Nandi, Asim Ghosh, and Anirban Chakraborti

Journal of Economics interaction and Coordination

We study unit-level expenditure on consumption across multiple countries and multiple years, in order to extract invariant features of consumption distribution. We show that the bulk of it is lognormally distributed, followed by a power law tail at the limit. The distributions coincide with each other under normalization by mean expenditure and log scaling even though the data is sampled across multiple dimension including, e.g. time, social structure and locations. This phenomenon indicates that the dispersions in consumption expenditure across various social and economic groups are significantly similar subject to suitable scaling and normalization. Further, the results provide a measurement of the core distributional features. Other descriptive factors including those of sociological, demographic and political nature, add further layers of variation on the this core distribution. We present a stochastic multiplicative model to quantitatively characterize the invariance and the distributional features.

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Journal Articles | 2017

Productivity differences and inter-state migration in the U.S.: A multilateral gravity approach

Anindya S. Chakrabarti and Aparna Sengupta

Economic Modelling

In this paper, we study the quantitative role of productivity differences in explaining migration in presence of multiple destination choices. We construct a dynamic general equilibrium model with multi-region, multi-sector set-up where labor is a mobile input, which adjusts to regional and sectoral productivity shocks, resulting in migration across regions. The proposed model generates a migration network where the flow of migrants between any two regions follows a gravity equation. We calibrate the model to the U.S. data and we find that variation in industrial and regional total factor productivity shocks explains about 63% of the interstate migration in the U.S. Finally, we perform comparative statics to estimate the effects of long-run structural changes on migration. We find that capital intensity of the production process and the demand for services over manufactured goods negatively impact aggregate level of migration whereas asymmetries in trade patterns do not appear to have substantial effects.

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Journal Articles | 2017

Accounting for political opinions, power, and influence: A Voting advice application

Tommi Pajala, Pekka Korhonen, Pekka Malo, Ankur Sinha, Jyrki Wallenius, and Akram Dehnokhalaji

European Journal of Operational Research

Voting Advice Applications (VAAs) are online decision support systems that try to match voters with political parties or candidates in elections, typically based on how each responds to a number of policy issue statements. Such VAAs play a major role in many countries. In this paper, we describe the development and large-scale application of a new innovative matching algorithm for the most widely used VAA in Finland. We worked closely with the owner of the VAA, the largest daily newspaper in Finland, Helsingin Sanomat. Their previous algorithm, which one might call a “naive” approach, was improved by including measures of candidate’s political power and influence, using proxy variables of media visibility and incumbency status. The VAA was implemented for the 2015 Parliamentary Election in Finland; our matching algorithm was used by 140,000 voters (26.7% of the electorate) in the Helsinki election district. The innovative algorithm generated recommendations that many voters were happy about, followed by users’ incidental comments that this was the first time the VAA recommended candidates they wanted to vote for. This showed the importance of catering to different kinds of voters with a model not previously considered by any VAA in any country.

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Journal Articles | 2017

A review on bilevel optimization: from classical to evolutionary approaches and applications

Ankur Sinha, Pekka Malo, and Kalyanmoy Deb

IEEE Computational Intelligence Society

Bilevel optimization is defined as a mathematical program, where an optimization problem contains another optimization problem as a constraint. These problems have received significant attention from the mathematical programming community. Only limited work exists on bilevel problems using evolutionary computation techniques; however, recently there has been an increasing interest due to the proliferation of practical applications and the potential of evolutionary algorithms in tackling these problems. This paper provides a comprehensive review on bilevel optimization from the basic principles to solution strategies; both classical and evolutionary. A number of potential application problems are also discussed. To offer the readers insights on the prominent developments in the field of bilevel optimization, we have performed an automated text-analysis of an extended list of papers published on bilevel optimization to date. This paper should motivate evolutionary computation researchers to pay more attention to this practical yet challenging area.

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Journal Articles | 2017

Optimal management of naturally regenerating uneven-aged forests

Ankur Sinha, Janne Ramo, Pekka Malo, and Olli Tahvonen

European Journal of Operational Research

A shift from even-aged forest management to uneven-aged management practices leads to a problem rather different from the existing straightforward practice that follows a rotation cycle of artificial regeneration, thinning of inferior trees and a clearcut. A lack of realistic models and methods suggesting how to manage uneven-aged stands in a way that is economically viable and ecologically sustainable creates difficulties in adopting this new management practice. To tackle this problem, we make a two-fold contribution in this paper. The first contribution is the proposal of an algorithm that is able to handle a realistic uneven-aged stand management model that is otherwise computationally tedious and intractable. The model considered in this paper is an empirically estimated size-structured ecological model for uneven-aged spruce forests. The second contribution is on the sensitivity analysis of the forest model with respect to a number of important parameters. The analysis provides us an insight into the behavior of the uneven-aged forest model.

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Journal Articles | 2017

Approximated set-valued mapping approach for handling multiobjective bilevel problems

Ankur Sinha, Pekka Malo, and Kalyanmoy Deb

Computers & Operations Research

A significant amount of research has been done on bilevel optimization problems both in the realm of classical and evolutionary optimization. However, the multiobjective extensions of bilevel programming have received relatively little attention from researchers in both the domains. The existing algorithms are mostly brute-force nested strategies, and therefore computationally demanding. In this paper, we develop insights into multiobjective bilevel optimization through theoretical progress made in the direction of parametric multiobjective programming. We introduce an approximated set-valued mapping procedure that would be helpful in the development of efficient evolutionary approaches for solving these problems. The utility of the procedure has been emphasized by incorporating it in a hierarchical evolutionary framework and assessing the improvements. Test problems with varying levels of complexity have been used in the experiments.

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Journal Articles | 2017

Evolutionary algorithm for bilevel optimization using approximations of the lower level optimal solution mapping.

Ankur Sinha, Pekka Malo, and Kalyanmoy Deb

European Journal of Operational Research

Bilevel optimization problems are a class of challenging optimization problems, which contain two levels of optimization tasks. In these problems, the optimal solutions to the lower level problem become possible feasible candidates to the upper level problem. Such a requirement makes the optimization problem difficult to solve, and has kept the researchers busy towards devising methodologies, which can efficiently handle the problem. Despite the efforts, there hardly exists any effective methodology, which is capable of handling a complex bilevel problem. In this paper, we introduce bilevel evolutionary algorithm based on quadratic approximations (BLEAQ) of optimal lower level variables with respect to the upper level variables. The approach is capable of handling bilevel problems with different kinds of complexities in relatively smaller number of function evaluations. Ideas from classical optimization have been hybridized with evolutionary methods to generate an efficient optimization algorithm for a wide class of bilevel problems. The performance of the algorithm has been evaluated on two sets of test problems. The first set is a recently proposed SMD test set, which contains problems with controllable complexities, and the second set contains standard test problems collected from the literature. The proposed method has been compared against three benchmarks, and the performance gain is observed to be significant. The codes related to the paper may be accessed from the website http://bilevel.org.

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