When the collective acts on its components: economic crisis
               autocatalytic percolation

Cantono, Simona; Solomon, Sorin

doi:10.1088/1367-2630/12/7/075038

Cited by 15 publications

(15 citation statements)

References 29 publications

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“…Thus, in many practical realizations, in addition to the bottom-up contagion propagation mechanisms one finds that there is a global-to-local feedback: individuals, their interdependence and behaviors build up the system that finally affects back on individuals' choices. It has been proposed that the bottom/up -top/down feedback has the capability to change completely the character of a phase transition from continuous to discontinuous, thus explaining the severity of the economic crises in systems where the collective interacts as such with its own components [24] (see also box 5 of [25]). …”

Section: Introduction and Overviewmentioning

confidence: 99%

Challenges in network science: Applications to infrastructures, climate, social systems and economics

Havlin

Kenett

Ben‐Jacob

et al. 2012

Eur. Phys. J. Spec. Top.

162

102

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Abstract. Network theory has become one of the most visible theoretical frameworks that can be applied to the description, analysis, understanding, design and repair of multi-level complex systems. Complex networks occur everywhere, in man-made and human social systems, in organic and inorganic matter, from nano to macro scales, and a e-mail: havlins@gmail.com b e-mail: drorkenett@gmail.com c e-mail: eshelbj@gmail.com d e-mail: armin.bunde@physik.uni-giessen.de e e-mail: reuven@macs.biu.ac.il f e-mail: hans@ifb.baug.ethz.ch g e-mail: jan.kantelhardt@physik.uni-halle.de h e-mail: kertesz@phy.bme.hu i e-mail: kirk@cs.huji.ac.il j e-mail: kurths@pik-potsdam.de k e-mail: juval@post.tau.ac.il l e-mail: sorin@vms.huji.ac.il 274The European Physical Journal Special Topics in natural and anthropogenic structures. New applications are developed at an ever-increasing rate and the promise for future growth is high, since increasingly we interact with one another within these vital and complex environments. Despite all the great successes of this field, crucial aspects of multi-level complex systems have been largely ignored. Important challenges of network science are to take into account many of these missing realistic features such as strong coupling between networks (networks are not isolated), the dynamics of networks (networks are not static), interrelationships between structure, dynamics and function of networks, interdependencies in given networks (and other classes of links, including different signs of interactions), and spatial properties (including geographical aspects) of networks. This aim of this paper is to introduce and discuss the challenges that future network science needs to address, and how different disciplines will be accordingly affected.

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Section: Introduction and Overviewmentioning

confidence: 99%

Challenges in network science: Applications to infrastructures, climate, social systems and economics

Havlin

Kenett

Ben‐Jacob

et al. 2012

Eur. Phys. J. Spec. Top.

162

102

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“…• the top process of global credit becoming scarce and interest rate increasing. 5. In 2008 the above problems become acute enough to be addressed by the regulators: the interest rate is exogenously cut down by the central bank in an attempt to stop the Minsky accelerator.…”

Section: A Momentmentioning

confidence: 99%

“…A model of the propagation of innovation [5] provided the basis for our model with autocatalytic feedback. Our model for testing the financial instability hypothesis incorporating an autocatalytic feedback loop between the proportion of ponzi firms and the interest rate was presented previously in [1].…”

Section: A Model Of Minsky's Financial Instability Hypothesismentioning

confidence: 99%

Too dynamic to fail: empirical support for an autocatalytic model of Minsky’s financial instability hypothesis

Golo

Brée

Kelman

et al. 2015

J Econ Interact Coord

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Solomon and Golo [1] have recently proposed an autocatalytic (self-reinforcing) feedback model which couples a macroscopic system parameter (the interest rate), a microscopic parameter that measures the distribution of the states of the individual agents (the number of firms in financial difficulty) and a peer-to-peer network effect (contagion across supply chain financing). In this model, each financial agent is characterized by its resilience to the interest rate. Above a certain rate the interest due on the firm's financial costs exceeds its earnings and the firm becomes susceptible to failure (ponzi). For the interest rate levels under a certain threshold level, the firm loans are smaller then its earnings and the firm becomes 'hedge.' In this paper, we fit the historical data (2002-2009) on interest rate data into our model, in order to predict the number of the ponzi firms. We compare the prediction with the data taken from a large panel of Italian firms over a period of 9 years. We then use trade credit linkages to discuss the connection between the ponzi density and the network percolation.We find that the 'top-down'-'bottom-up' positive feedback loop accounts for most of the Minsky crisis accelerator dynamics. The peer-to-peer ponzi companies contagion becomes significant only in the last stage of the crisis when the ponzi density is above a critical value. Moreover the ponzi contagion is limited only to the companies that were not dynamic enough to substitute their distressed clients with new ones. In this respect the data support a view in which the success of the economy depends on substituting the static 'supply-network' picture with an interacting dynamic agents one.

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“…In that model, the swings between various market moods are explained in terms of the influence that the peer groups are exercising on each of their members. The approach of the collective as an entity has been advocated also in [Cantono 2010], [Cantono 2012], [Biondi 2005], [Biondi 2010].…”

Section: Minsky's Scenario and The Role Of Ponzi Unitsmentioning

confidence: 99%

Minsky Financial Instability, Interscale Feedback, Percolation and Marshall–Walras Disequilibrium

Solomon¹,

Golo

2013

Accounting, Economics and Law

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We study analytically and numerically Minsky instability as a combination of top-down, bottom-up and peer-to-peer positive feedback loops. The peer-to-peer interactions are represented by the links of a network formed by the connections between firms; contagion leading to avalanches and percolation phase transitions propagating across these links. The global parameter in the top-bottom -bottom-up feedback loop is the interest rate. Before the Minsky Moment, in the 'Minsky loans accelerator' stage the relevant "bottom" parameter representing the individual firms' micro-states, is the quantity of loans. After the Minsky Moment, in the 'Minsky crisis accelerator' stage, the relevant 'bottom' parameters are the number of ponzi units / quantity of failures / defaults. We represent the top-bottom, bottom-up interactions on a plot similar to the Marshall-Walras diagram for quantity-price market equilibrium (where the interest rate is the analog of the price). The Minsky instability is then simply emerging as a consequence of the fixed point (the intersection of the supply and demand curves) being unstable (repulsive). In the presence of network effects, one obtains more than one fixed point and a few dynamic regimes (phases). We describe them and their implications for understanding, predicting and steering economic instability.

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When the collective acts on its components: economic crisis autocatalytic percolation

Cited by 15 publications

References 29 publications

Challenges in network science: Applications to infrastructures, climate, social systems and economics

Challenges in network science: Applications to infrastructures, climate, social systems and economics

Too dynamic to fail: empirical support for an autocatalytic model of Minsky’s financial instability hypothesis

Minsky Financial Instability, Interscale Feedback, Percolation and Marshall–Walras Disequilibrium

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