Posts Tagged ‘conferences’

Wine & Economics

November 30, 2009

Just received this call for papers:

Dear wine friends,
the American Association of Wine Economists (AAWE) will hold its 4th Annual Conference from June 25-28, 2010, at UC Davis in California.
The conference will be hosted by UC Davis and the Robert Mondavi Institute for Wine and Food Science. All economics and statistics papers related to wine and food are welcome.

Details will be posted on our website at
www.wine-economics.org

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Looking forward to next year’s EEA…

November 13, 2009

…which will take place in Philadelphia in February. I will present at the Agent-Based Economics Session (organized by Jason Barr).

More information on the conference can be found on the EEA homepage.

Extended Abstract

We build a computational model of heterogeneous agents playing a public good game on a dynamic network. In public good games the unique Nash equilibrium leads to a suboptimal pro- vision of public goods. Giving rewards to contributors transforms the game but gives rise to a second-order dilemma. By allowing for coevolution of strategies and network structure the adaptive dynamics operate on both structure and strategy. Agents learn with whom to interact and how to act and can overcome the second-order dilemma. The key variable of interest is the long-run frequency of contribution to the public good.

The public good game with rewards is a two-stage game where agents choose to contribute to the public good in the first stage and have the possibility to give rewards to direct neighbors in the second stage. Rewards are assigned based on both social distance and the quality of the contribution. We consider different network topologies under different imitation strategies with syncronous and asyncronous updating and analyze their impact on the long-run frequency of contributions to the public good.
Evolutionary models with local interactions on static networks have been discussed analyt- ically (see Eshel et al. (1998) for a circle network, Albin and Foley (2001) and Nowak and May (1993) for a two-dimensional lattice, and Watts (1999, chapter 8 ) for small-world networks). Dy- namic networks have recently been discussed by Skyrms and Pemantle (2000), Alexander (2007, chapter 3.5) and Jun and Sethi (2009). We build on this literature by examining the long-run frequency of cooperation in public good games with rewards played on dynamic, directed networks.

The model captures several stylized facts:

  1. In modern societies people can, at least to some extent, select their partners. Links may be altered when individuals move to another neighborhood, city or country. We take this into account by allowing the network to evolve. Specifically, we allow agents to break a link with a direct neighbor if this link becomes weak relative to the agent’s other links.
  2. The effect of rewards on prosocial behavior has been discussed theoretically (Ellingsen and Johannesson, 2007; Benabou and Tirole, 2006; Brennan and Pettit, 2000; Hollaender, 1990) and experimentally (Masclet et al., 2003; Gaechter and Fehr, 1999). In our model rewards create a selective incentive for contribution. They strengthen the links between contributors so that the benefits of the rewards are enjoyed primarily be cooperators and prosocial behavior can survive selectionary pressure.
  3. If the quality of other agent’s contributions is observable agents with higher abilities will be more likely to receive rewards. This creates an endogeneous feedback mechanism inducing skilled agents to contribute more often. Agents with low ability will eventually stop contributing and individual agents self-select.

References

  • Albin, Peter S. and Duncan K. Foley, “The Co-Evolution of Cooperation and Complexity in a Multi-player, local-interaction Prisoner’s Dilemma,” Complexity, 2001, 6 (3), 54–63.
  • Alexander, Jason McKenzie, The Structural Evolution of Morality, Cambridge University Press, 2007. Benabou, Roland and Jean Tirole, “Incentives and Prosocial Behavior,” American Economic Review, 2006, 96 (5), 1652–1678.
  • Brennan, Geoffrey and Phillip Pettit, “The hidden economy of esteem,” Economics and Philosophy, 2000, 16 (1), 77–98.
  • Ellingsen, Tore and Magnus Johannesson, “Paying Respect,” Journal of Economic Perspectives, 2007, 21 (4), 135–149.
  • Eshel, Ilan, Larry Samuelson, and Avner Shaked, “Altruists, Egosists, and Hooligans in a Local Interaction Model,” American Economic Review, 1998, 88 (1), 157–179. Gaechter, Simon and Ernst Fehr, “Collective action as a social exchange,” Journal of Economic Behavior and Organization, 1999, 39 (4), 341–369.
  • Hollaender, Heinz, “A Social Exchange Approch to Voluntary Cooperation,” American Economic Review, 1990, 80 (5), 1157–1167.
  • Jun, Tackseung and Rajiv Sethi, “Reciprocity in evolving social networks,” Journal of Evolutionary Economics, 2009, 119 (3), 379–396.
  • Masclet, David, Charles Noussair, Steven Tucker, and Marie-Claire Villeval, “Monetary and Non- monetary Punishment in the Voluntary Contributions Mechanism,” American Economic Review, 2003, 93 (1), 366–380.
  • Nowak, Martin A. and Robert M. May, “The spatial dilemmas of evolution,” International Journal of Bifurcation and Chaos, 1993, 3 (1), 35–78.
  • Skyrms, Brian and Robin Pemantle, “A Dynamic Model of Social Network Formation,” Proceedings of the National Academy of Sciences, 2000, 97 (16), 9340–9346.
  • Watts, Duncan J., Small Worlds – The Dynamics of Networks between Order and Randomness Princeton Studies in Complexity, Princeton University Press, 1999.

June 14, 2009

Next weekend I’m heading to Minneapolis to present at the Second Biennial Conference of the Society for Philosophy of Science in Practice (SPSP)

Increasing Returns in Science
A Formal Model of Consensus and Dissensus

Abstract: We construct a formal model describing the dynamics of science under increasing returns. The model restates the problem of the division of labor in science as an attempt to bring in increasing returns to the dynamics of science. By assuming increasing returns our model differs significantly from Kitcher (1990) and is closer to a series of models originally developed by Brian Arthur (1994). While Arthur’s models describes the economics of technology choice, we demonstrate that a similar model can be used to replicate the dynamics of science.

We build an abstract computational agent-based model. In our model there is a population of heterogeneous scientists. Their main activity is to produce evidence. By producing evidence (e.g. writing a paper) each scientist employs the methods of a particular ‘school of though’, ‘paradigm’, or cluster. The decisons at the micro-level produce a particular pattern at the macro-level. Several ‘schools of thought’, or clusters exist side by side (diversity), or one cluster gets dominant (specialization) with several smaller clusters relegated to the fringes.

The individual scientist does not directly react to a objective world but to the available evidence produced by his fellow scientists. He relies on his colleagues’ testimony. His decision – specialize or diversify – is based on his own preferences and the available evidence produced by his fellows. This introduces a kind of herd behavior where, under certain conditions, uniformity of opinions emerges as a result of positive feedback effects. In Arthur’s model the corresponding situation would be a lock-in: all producers adopting the same (potentially ineffective) technology. In the dynamics of science, however, we rarely find uniformity of opinions. There are always some sceptics out there, opposing conventional wisdom. We take this into account and tune our model so that complete lock-in is only a special case. In the more general case of the model we see a dominant cluster besides several small ones. The process in which one cluster gets dominant is path-dependent and nonergodic. Random events are not averaged away over as time passes, and small fluctuations matter for the selection of the dominant cluster. Although we cannot predict which cluster will get dominant, we know that one cluster will get dominant for sure, hence the process is predictable.

By modeling the scientist’s choice as a nonlinear Polya process we take into account increasing returns. The strength of the increasing returns effect depend on available evidence as well as on the strength of clusters. Within stronger clusters scientists are more likely to stick to the accepted methods and specialize. In addition to the effect of a cluster’s strength and evidence there are the scientist’s preferences. By making a contribution to a cluster a scientist invests time and money. These sunk costs lead to a change in the scientist’s preference, making the agent more likely to contribute to the same cluster again. Or more pithily: higher sunk costs make it more likely that scientists specialize.

By calibrating our model we are able to explain both, the formation of consensus and the dissolution of consensus. We explore the parameter space of the model and look how institutional factors and policy influence the dynamics of the model. In particular, we look at policies that can effectively reduce the CO-IR discrepancy. Using some examples from the history of economic thought we link our abstract model to particular periods in the history of science.

Interacting Heterogeneous Agents Produce Endogeneous Inequality

November 3, 2008

INTERACTING HETEROGENEOUS AGENTS PRODUCE
ENDOGENOUS INEQUALITY


by MATTHIAS GREIFF, STEPHEN KINSELLA, AND EDWARD J. NELL

Here are the slides from my presentation!

Abstract: We model an abstract economy of locally interacting heterogeneous agents in four markets, to understand the generation of power law-type distributions of income inequality and firm size in advanced societies. We
model a macroeconomy with national accounts built from the interactions of agents (workers, capitalists, bankers, and the government) in time through product, labour, bond, and money markets. We show that, without any restrictions on the type of interaction agents can make, and with asymmetric information on the part of capitalists and workers in this economy, power-law dynamics with respect to firm size and income can emerge from simple multiplicative processes originating in the labour market. Using a new data set, we
use only one free parameter to fit the models to firm size and income data for Ireland from 2000 to 2006.

(presented at the EEA annual meeting 2009, New York)

Increasing returns in scientific knowledge

August 20, 2008

presented at the Workshop on Formal Modeling in Social Epistemology, Tilburg University

Rogier De Langhe
Centre for Logic and Philosophy of Science
Ghent University, Belgium

Matthias Greiff
Institute for Institutional and Innovation Economics
University of Bremen, Germany

We present a model that builds further on Philip Kitcher’s 1990 paper “The Division of Cognitive Labor”. We argue that Kitcher has made a valuable contribution in framing the problem and presenting a solution, but claim that his results have only limited scope because the scientific community is modelled as a closed system, with a definite ending point and decreasing marginal returns as the endpoint nears. In contrast, we present a model based on increasing returns, drawing on the literature of increasing returns models in institutional economics, in particular the formalism developed in a series of papers by Brian Arthur.

KEY REFERENCES

  • Arthur, Brian (1989), ‘Competing technologies, increasing returns, and lock-in by historical events’, The Economic Journal, 99, pp. 116-31
  • Arthur, Brian (1994). Increasing returns and path-dependence in the economy. University of Michigan Press
  • Kitcher, Philip (1990), ‘The division of cognitive labor’, The Journal of Philosophy, 87(1), pp. 5-22

Complexity, Uncertainty, and the Emergence of Cooperation

June 15, 2008

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