[This paper was written for my Biological and Social Structures class in 2015. Significant inputs were provided by Prof. Niceto S. Poblador of the UP School of Economics. I think it will be a waste of effort if no one will hear about this idea, how crazy it may seem. Comments are appreciated]
Regulation in biological systems and its applicability to credit derivative industry towards resilient financial market
Financial systems are prone to system collapse which in the case of the 2008 market breakdown attributed to the unregulated proliferation of credit derivatives. On the other hand cell regulatory mechanisms allow robust processes to occur amidst stress and this is where social systems can draw insights from. This paper explores the applicability of the cell regulatory mechanisms that will accord robustness to the financial network. Universal network patterns and emergent structure mirror stability or instability of a system. For this reason, we start by describing large-scale topological features of the two seemingly dissimilar systems – biological and financial systems. We next described the dynamics of regulation of the biological cell that might confer robustness and resilience to financial system. This hypothesis paper hopes to advance and promote the discussion on the congruence of biological and social structures as a tool to create a framework for economic and social change.
Keywords: biological robustness, resilience, complex network, financial crisis, credit derivative
There have been numerous global financial crises (Jorda, Schularick, & Taylor, 2011; Reinhart & Rogoff, 2008). Just this decade the world has experienced two financial disasters – the 2001 dot-com bubble and recently, the 2008 subprime crisis (Boorman, 2009; Goodnight & Green, 2010; Lawson, 2009; Ljungqvist & Wilhelm, 2003; Schularick & Taylor, 2009). The 2008 global financial crisis crippled the world economy causing massive unemployment and company bankruptcy all over the world (Boorman, 2009; Tett, 2010). In a highly globalized economy, the interconnectedness of bank and financial institutions drives the world economy to freeze given the failure of one of its core constituents (Battiston, Puliga, Kaushik, Tasca, & Caldarelli, 2012; Squartini, van Lelyveld, & Garlaschelli, 2013; Terzi & Uluçay, 2011; Vitali, Glattfelder, & Battiston, 2011). Furthermore, the recent crisis is attributed to the unregulated increase in the risky financial instrument known as credit derivatives (Calistru, 2012; Instefjord, 2005; Puliga, Caldarelli, & Battiston, 2014). Excessive transactions of credit derivatives and corporate corruption point towards regulation to create harmony and to reduce the number and impact of future financial catastrophe. Regulations and incentives in place were not optimal and hence need to be periodically adapted to continuously changing circumstances. Some economists believe that banking deregulation is the major cause of the 2008 US Financial crisis (Daniel & Jones, 2007; Jeffers, 2013), for instance, the repulsion of Glass-Steagall Act and the Commodity Futures Modernization Act. The Glass-Steagall Act prohibits the merger of financial institutions while Commodity Futures Modernization Act promotes regulation of financial products such as derivatives (Sherman, 2009). Although this is the dominant ideology there are studies that show that financial liberalization results to higher cost efficiency and cheaper services to clients (Andries & Capraru, 2013).
In the attempt to understand the systemic risk involved in dealing with the highly complex financial instruments such as the credit derivative, the US National Academies/National Research Council and New York Federal Reserve conducted a conference in 2006 to motivate discussion on systemic risk and similar occurrences in other disciplines such as engineering and ecology (Kambhu, Weidman, & Krishnan, 2007; May, Levin, & Sugihara, 2008). Similarly, another study has shown that analogies and ultimately financial policies can be drawn from ecological food webs and the spread of infectious diseases (Haldane & May, 2011). This paper will further extend the concept and will seek to answer the applicability of cell regulatory mechanisms that will confer robustness and resilience to the ever unstable financial market. This paper will attempt to draw regulatory patterns in biological systems, in which the cell will be used as a model system for an efficient and self-regulatory unit. In contrast to the financial market often characterized by its sensitivity to systemic risk.
Credit derivative (CD) is defined as “a class of privately negotiated contracts designed with the express purpose of transferring credit risk from one party to another” (Calistru, 2012). At first, this seems an innovative way to make the market safer through dispersing risks as have been put forward by its pioneer. However, CD does not eliminate risk it just transfers risk, hence whenever market fails, financial institutions will eventually lose money (Calistru, 2012). By the virtue of risk transfer, CD allows the bank to obtain high leverage and hence to lower capital reserve. Moreover, CD creates more financial dependencies hence connection among financial institutions. In a complex network perspective, this reduces modularity and hence decreases system robustness. Two types of CDs were prominent in the market prior to the crisis – the credit default swaps (CDS) and mortgage-backed securities (MBS) (Figure 1).
In an example given by Tett (2010), CDS starts with a loan transaction, in this example, the bank (JPMorgan) and a client (Exxon) engaged in a loan agreement. Since there’s always a risk of default the bank finds a third party institution that will assume credit risk, in this case, EBRD. In return, the bank will pay the insurer a fee. CDS works similar to insurance however unlike insurance the insurer does not need to maintain a level of capital reserve. Whereas mortgage-backed securities work similar to CDS, but instead of the bank paying a fee, the mortgage acquired from the home buyer is used to pay the insurer. Since this system is profitable, the demand for housing loans increases. This became a toxic transaction, known as subprime loans, in which lenders gave loans even to those home buyer that are not capable of paying. Eventually triggering the collapse of financial market in 2008 (Demyanyk & Van Hemert, 2011).
The shortcomings of the efficient market hypothesis
The traditional economic theory states that the market is efficient (Lo, 2004; Malkiel, 2003). Markets are thought to correctly assess the risk and profitability of transactions (Catanzaro & Buchanan, 2013; Lo, 2004; Malkiel, 2003). Some believe that there’s a natural cycle of boom and bust and that the economy can heal itself reminiscent of the argument of the climate change detractors. Some economists also believe in deregulation, they believe that when the government imposes regulation it hinders innovation and productivity in the market (Eliasson, 1991; Jayaratne & Strahan, 1996; Shapiro, 2014). However, it has been shown that traditional financial models fail (Buchanan, 2009; Lo, 2004). One major factor that needs to be incorporated is the irrational human behavior (Mullainathan & Thaler, 2000; Wald, 2008). Two studies give hints on how people behave in the market. A study suggests that when people are exposed to market transactions (i.e. trading) the subject’s morality declined (Falk & Szech, 2013). In this experiment, the subject has an option to transact and earn money and by doing so knowingly a mouse will be killed. Another option is not engaging on a transaction and therefore saving the life of the mouse. It seems that based on this study that markets erode moral values. Another study on the dishonesty in the banking industry suggests that bank employees are honest in normal condition but not when their identity as a banker is rendered salient (Cohn, Fehr, & Marechal, 2014). The point of these studies is not to question market economies in general but to understand the factors affecting dishonest behavior in order to develop possible interventions. Furthermore, since economic agents are driven by self-interest, governance or control mechanism designed to provide incentives to pursue social goals as a means for achieving selfish ones is needed. Likewise, knowing that markets are prone to collapse due to a variety of factors, including human irrational behavior, regulatory mechanisms are imperative in order to exist an efficient flow of money in the economy.
On robustness and resilience of biological systems
Biological systems are robust (Kitano, 2004; Stelling, Sauer, Szallasi, Doyle, & Doyle, 2004). It offers an exemplary model not far from those of financial networks where one can gain an understanding of the mechanisms allowing a system to cope against perturbations. Robustness is defined as the “property of a system that allows a system to maintain its functions despite external and internal perturbations” (Kitano, 2004). A property attained through a long process of adaptation. The author furthered explained that robust biological designs are characterized by four major mechanisms. First, robust systems have a high degree of system control which can be attained via a feedback loop which amplifies or dissipate stimulus. Second, redundancy and diversity confer robustness to biological systems. Redundancy refers to the presence of identical components in the system while diversity refers to the non-identical entity with similar functions hence functional redundancy. The third and last mechanisms are modularity and decoupling which reduces the effect of distress to the whole system. Drawing analogy from these mechanisms, this paper aims to put forward regulatory mechanism that will confer robustness, and subsequently resilience to financial markets.
This hypothesis paper aims to determine similarities in regulatory patterns in the cell and financial markets. Specifically, determine cell regulatory mechanisms that are applicable to financial markets that will ultimately confer robustness and resilience to the financial industry. Questions on the applicability will be elucidated to stimulate the discussion on science-based policies against financial disaster. Although it is tempting to extrapolate financial controls across different geopolitical regions it is important to note that most of the analogies in this paper were drawn from the US financial market which is inherently different from that of the Philippines. Moreover, establishing and implementing policies requires a high degree of collaborative effort. Unless there exists a collective action that leads to a set of adaptable rules intended to achieve overall system objectives, socially desirable emergent patterns will fail to materialize.
The shared properties, both inherent similarities, and differences that characterize a complex network allow comparison of universal patterns across these systems. Similarly, interactions among agents and the emerging structure determine the stability or instability of a system. Moreover, a sudden shift and system collapse can be realized in the network topology. Thus in order for us to draw insights from biological networks, it is vital that we start at the large-scale structure – to describe the topology of both systems and determine universal law governing the emergence of such topology.
Network topology of cell regulatory network and financial networks
Financial systems can be considered as a complex network where nodes are financial institutions and links are financial dependencies (Battiston, Caldarelli, Georg, May, & Stiglitz, 2013) (Figure 2a). Connections or dependencies can be liability, exposure, ownership or merely correlation (Caldarelli, Chessa, Pammolli, Gabrielli, & Puliga, 2013). Often the financial institutions are described as highly interconnected. The concentration and connectedness of financial institution rendered the system prone to systemic risk. Systemic risk is define “as the default of a large portion of the financial system” (Battiston et al., 2012) which can also be quantified using networks.
Market dynamics are often describe as stochastic and nonlinear systems. In one study conducted by Battiston et al (2012), some banks exhibited increased in centrality (i.e. node importance to the system) during the build-up of the crisis. Centrality is measured using an algorithm called DebtRank indicator. They have shown that central institutions that are systematically important can be identified from a network science perspective. Another effect of credit derivative is the increase in complexity. Contrary to ecological diversity, increasing complexity of the network leads to instability as stipulated by the May-Wigner theorem (Battiston et al., 2013; May, 1972). In the case of the 2008 collapse, in order to drive profit, there was an increase in complexity of transactions at the expense of stability. This created the so called small world effect. Credit derivative transactions, reduces distances between nodes hence easier stress propagation (Caldarelli et al., 2013). In a game-theoretic framework, these models assume a known set of self-seeking strategies among the players, and a given set of rules to legitimize their behavior. Thus, solutions can be found algorithmically. Absent a learning dynamics by which strategies and rules adapt to changing circumstances, such systems eventually collapse. Collective action, or collaborative strategies are clearly called for in order to achieve mutually beneficial results.
Whereas features of biological network are inherently different from financial network. Biological networks include protein-protein interaction, transcription regulatory, metabolic and signaling networks (Barabasi & Oltvai, 2004). The architecture of cellular networks are mostly scale-free following a power law with some having a mix of scale-free and exponential features (Wuchty, Ravasz, & Barabási, 2006) (Figure 2b). Biological networks were also observed to be disassortive, meaning highly connected nodes (or hubs) seldom connects with another hub (Barabasi & Oltvai, 2004; Wuchty et al., 2006). Modularity and clustering are detected in almost all biological networks. The concept that topology determines robustness also applies to biological networks. In the case of the scale-free biological network, random failures will usually affect a small portion of the system. For instance, approximately 20% of gene deletion is lethal for Escherichia coli (Gerdes et al., 2003), Saccharomyces cerevisiae (Jeong, Mason, Barabasi, & Oltvai, 2001) and Mycoplasma genitalium (Glass et al., 2006). However, the presence of hubs poses a problem called attack vulnerability, hubs deletion might break the network into several small isolated clusters (Barabasi & Oltvai, 2004).
Analogy in cell and financial industry regulation
Although knowledge of a system can be derived from the network topology, it has its own limitations. To complicate things, non-topological features affects systems collapse hence it should also be examined. The dynamics of a system and functions of the links need to be elucidated. The next section will look closely on the mechanisms that will drive a system towards robustness and resilience.
Protein ubiquitination and credit rating
One of the apparent similarities of both systems in terms of regulation is through quality control. There are agencies that review financial loans. In the US there exists the big three – Moody’s, Fitch, and Standard and Poor’s. These agencies suggest rating with regards to the risk and safety level of the loans and transaction (Cantor & Packer, 1994). However, the problem with these rating agencies being self-seeking agents is their susceptibility to regulatory capture like their counterparts in government. In the case of biological systems, cell maintains the right quantity of functional protein through protein ubiquitination (Komander & Rape, 2012; Pickart & Eddins, 2004). Whenever a protein is misfolded, the cell uses a tag called the ubiquitin which recruits another protein to degrade the denatured proteins. This process can be regarded as a collaborative strategy ultimately aimed at optimal system performance. In an extent, the role of protein ubiquitination is similar to that of credit rating agency, to filter out those transactions that are toxic and has a high tendency to default. There is a big reliance on regulators to credit rating. However, as seen in the recent crisis there is a tendency to misuse credit ratings on top of conflict of interest problem. Government oversight on credit agency and transparency are suggested solutions to these fraudulent practices.
Combinatorial regulatory agents and functional diversity
Diversity and redundancy accord robustness to systems (May et al., 2008). The cell accomplishes redundancy by means of several mechanisms. In transcription, multiple promoters and enhancer regions allow flexibility and expression control (Ayoubi & Van De Ven, 1996). In addition, small regulatory RNA known as microRNA, because of its short sequence allows regulation of a wide range of targets (Ebert & Sharp, 2012) similar to that of promiscuous proteins (Aharoni et al., 2005; DePristo, 2007). To an extent, lessons from biological redundancy can be extrapolated to financial transactions. Diversification of assets and multiple checkpoints in a transaction are classic examples. Prior to the 2008 crisis, the profit-driven by financial innovation blinded the bankers into taking risky investments and hence needs to be addressed, for instance through multiple regulatory checkpoints.
Regulatory feedback loops
Feedback loops are common to almost all system, biological to machines. In biological systems, multiple feedback loops are important in system stability for instance in signal transduction systems (Gong & Zhang, 2010), p53 pathway (Harris & Levine, 2005) and gene regulation (Mengel, Krishna, Jensen, & Trusina, 2012; Srobar, 2004). A feedback loop occurs when an output of circuit affect the production of itself either promote (positive feedback) or inhibits (negative feedback) the system (Sneppen, Krishna, & Semsey, 2010). Feedback and feedforward loops in social systems are mainly in the form of information flows, which, in our Knowledge-Driven world, continues to be restricted by deeply rooted institutional factors that tend to perpetuate information asymmetries. In order for this to be advantageous to financial industry, one needs to identify negative feedback loops to control the proliferation of toxic transactions, a self-regulatory unit of some sort.
Cell and financial market compartmentalization
Compartmentalization in the cell allows the spatial regulation of certain processes (Klitgord & Segre, 2010; Martin, 2010). On a network science perspective, cell compartmentalization increase modularity. This has been the basis of many financial laws such as the Glass-Steagall Act which prevents the merger of financial institutions (Perkins, 1991). In addition, in order to diminish systemic risk, we can limit the scope of business the bank can engage in. On a socio-political aspect, we should also restrict the influence of the financial sector to government policies. However, the downside in social systems, modularity prevents the bringing together of complementary resources – especially knowledge – which is essential in achieving network effects in the creation of economic value.
There are fundamental differences in the network topology of the biological cell and financial industry. Still, there are regulatory mechanisms that are common between the two systems. Insights on policies can be gained from complex systems approach especially if one can shape the topology of financial network similar to that of the biological network (i.e. diversity, redundancy and modularity in financial network). How these similarities translate to science-based policies is probably the challenge to regulators. There also a notion of controllability in a network (Galbiati, Delpini, & Battiston, 2013; Liu, Slotine, & Barabasi, 2011) which could be of value to the implementation of these policies. Theories presented in this paper might help in the modeling of financial collapse and its regulation, and eventually mitigate future disasters. On a final note, biological robustness arises due to evolution (Kitano, 2004). As anticipated, strict regulations will not be welcomed in financial industry. Maybe it is time for the whole industry to evolve or face the consequence – that is possible extinction.
A big thanks to the MS 397 Biological and Social Structure class (2nd semester AY 2014-2015) spearheaded by Helen T. Yap and Gisela P. Concepcion, along with all the lecturers for promoting discussions on complexity theory, biological structures, and social systems.
Aharoni, A., Gaidukov, L., Khersonsky, O., Gould, S. M., Roodveldt, C., & Tawfik, D. S. (2005). The ‘evolvability’ of promiscuous protein functions. Nat Genet, 37(1), 73-76. doi: http://www.nature.com/ng/journal/v37/n1/suppinfo/ng1482_S1.html
Andries, A. M., & Capraru, B. (2013). Impact of Financial Liberalization on Banking Sectors Performance from Central and Eastern European Countries. PLoS One, 8(3), e59686. doi: 10.1371/journal.pone.0059686
Ayoubi, T. A., & Van De Ven, W. J. (1996). Regulation of gene expression by alternative promoters. The FASEB Journal, 10(4), 453-460.
Barabasi, A. L., & Oltvai, Z. N. (2004). Network biology: understanding the cell’s functional organization. Nat Rev Genet, 5(2), 101-113. doi: 10.1038/nrg1272
Battiston, S., Caldarelli, G., Georg, C.-P., May, R., & Stiglitz, J. (2013). Complex derivatives. Nature Physics, 9(3), 123-125. doi: 10.1038/nphys2575
Battiston, S., Puliga, M., Kaushik, R., Tasca, P., & Caldarelli, G. (2012). DebtRank: too central to fail? Financial networks, the FED and systemic risk. Sci Rep, 2, 541. doi: 10.1038/srep00541
Boorman, J. (2009). The Current Financial Crisis: Its Origins, Its Impact, and the Needed Policy Response. Global Journal of Emerging Market Economies, 1(2), 127-135. doi: 10.1177/097491010900100202
Buchanan, M. (2009). Economics: Meltdown modelling. Nature, 460, 680-682. doi: doi:10.1038/460680a
Caldarelli, G., Chessa, A., Pammolli, F., Gabrielli, A., & Puliga, M. (2013). Reconstructing a credit network. Nature Physics, 9(3), 125-126. doi: 10.1038/nphys2580
Calistru, R. A. (2012). The Credit Derivatives Market – A Threat to Financial Stability? Procedia – Social and Behavioral Sciences, 58, 552-559. doi: 10.1016/j.sbspro.2012.09.1032
Cantor, R., & Packer, F. (1994). The credit rating agency. Federal Reserve Bank of New York Quarterly Review, 19(2), 1-26.
Catanzaro, M., & Buchanan, M. (2013). Network opportunity. Nature Physics, 9(3), 121-123. doi: 10.1038/nphys2570
Cohn, A., Fehr, E., & Marechal, M. A. (2014). Business culture and dishonesty in the banking industry. Nature, 516(7529), 86-89. doi: 10.1038/nature13977
Daniel, B. C., & Jones, J. B. (2007). Financial liberalization and banking crises in emerging economies. Journal of International Economics, 72(1), 202-221. doi: 10.1016/j.jinteco.2006.07.009
Demyanyk, Y., & Van Hemert, O. (2011). Understanding the Subprime Mortgage Crisis. Review of Financial Studies, 24(6), 1848-1880.
DePristo, M. A. (2007). The subtle benefits of being promiscuous: Adaptive evolution potentiated by enzyme promiscuity. HFSP Journal, 1(2), 94-98. doi: 10.2976/1.2754665
Ebert, M. S., & Sharp, P. A. (2012). Roles for microRNAs in conferring robustness to biological processes. Cell, 149(3), 515-524. doi: 10.1016/j.cell.2012.04.005
Eliasson, G. (1991). Deregulation, innovative entry and structural diversity as a source of stable and rapid economic growth. Journal of Evolutionary Economics, 1(1), 49-63. doi: 10.1007/BF01202338
Falk, A., & Szech, N. (2013). Morals and Markets. Science, 340(6133), 707-711.
Galbiati, M., Delpini, D., & Battiston, S. (2013). The power to control. Nature Physics, 9(3), 126-128. doi: 10.1038/nphys2581
Gerdes, S. Y., Scholle, M. D., Campbell, J. W., G., B., Ravasz, E., Daugherty, M. D., . . . Osterman, A. L. (2003). Experimental determination and system level analysis of essential genes in Escherichia coli MG1655. J. Bacteriol., 185(19).
Glass, J. I., Assad-Garcia, N., Alperovich, N., Yooseph, S., Lewis, M. R., Maruf, M., . . . Venter, J. C. (2006). Essential genes of a minimal bacterium. Proc Natl Acad Sci U S A, 103(2), 425-430. doi: 10.1073/pnas.0510013103
Gong, Y., & Zhang, Z. (2010). Network robustness due to multiple positive feedback loops: a systematic study of a Th cell differentiation model. Signal Transduction Insights(2).
Goodnight, G. T., & Green, S. (2010). Rhetoric, Risk, and Markets: The Dot-Com Bubble. Quarterly Journal of Speech, 96(2), 115-140. doi: 10.1080/00335631003796669
Haldane, A. G., & May, R. M. (2011). Systemic risk in banking ecosystems. Nature, 469(7330), 351-355. doi: 10.1038/nature09659
Harris, S. L., & Levine, A. J. (2005). The p53 pathway: positive and negative feedback loops. Oncogene, 24(17), 2899-2908. doi: 10.1038/sj.onc.1208615
Instefjord, N. (2005). Risk and hedging: Do credit derivatives increase bank risk? Journal of Banking & Finance, 29(2), 333-345. doi: 10.1016/j.jbankfin.2004.05.008
Jayaratne, J., & Strahan, P. E. (1996). The Finance-Growth Nexus: Evidence from Bank Branch Deregulation. The Quarterly Journal of Economics, 111(3), 639-670. doi: 10.2307/2946668
Jeffers, E. (2013). Banking Deregulation and the Financial Crisis in the US and France. Comparative Economic Studies, 55(3), 479-500. doi: 10.1057/ces.2013.13
Jeong, H., Mason, S. P., Barabasi, A. L., & Oltvai, Z. N. (2001). Lethality and centrality in protein networks. Nature, 411(6833), 41-42.
Jorda, O., Schularick, M., & Taylor, A. M. (2011). Financial Crises, Credit Booms, and External Imbalances: 140 Years of Lessons. IMF Economic Review, 59(2), 340-378. doi: 10.1057/imfer.2011.8
Kambhu, J., Weidman, S., & Krishnan, N. (2007). New Directions for Understanding Systemic Risk. Econ. Policy Rev., 13(2).
Kitano, H. (2004). Biological robustness. Nat Rev Genet, 5(11), 826-837. doi: 10.1038/nrg1471
Klitgord, N., & Segre, D. (2010). The importance of compartmentalization in metabolic flux models: yeast as an ecosystem of organelles. Genome Inform, 22, 41-55.
Komander, D., & Rape, M. (2012). The ubiquitin code. Annu Rev Biochem, 81, 203-229. doi: 10.1146/annurev-biochem-060310-170328
Lawson, T. (2009). The current economic crisis: its nature and the course of academic economics. Cambridge Journal of Economics, 33(4), 759-777. doi: 10.1093/cje/bep035
Liu, Y. Y., Slotine, J. J., & Barabasi, A. L. (2011). Controllability of complex networks. Nature, 473(7346), 167-173. doi: 10.1038/nature10011
Ljungqvist, A., & Wilhelm, W. J. (2003). IPO Pricing in the Dot-com Bubble. The Journal of Finance, 58(2), 723-752. doi: 10.1111/1540-6261.00543
Lo, A. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Porfolio Theory.
Malkiel, B. G. (2003). The Efficient Market Hypothesis and Its Critics. Journal of Economic Perspectives, 17(1), 59-82.
Martin, W. (2010). Evolutionary origins of metabolic compartmentalization in eukaryotes. Philos Trans R Soc Lond B Biol Sci, 365(1541), 847-855. doi: 10.1098/rstb.2009.0252
May, R. M. (1972). Will a Large Complex System be Stable? Nature, 238(5364), 413-414.
May, R. M., Levin, S. A., & Sugihara, G. (2008). Complex systems: Ecology for bankers. Nature, 451(7181), 893-895.
Mengel, B., Krishna, S., Jensen, M. H., & Trusina, A. (2012). Nested feedback loops in gene regulation. Physica A: Statistical Mechanics and its Applications, 391(1-2), 100-106. doi: 10.1016/j.physa.2011.06.019
Mullainathan, S., & Thaler, R. H. (2000). Behavioral Economics. National Bureau of Economic Research Working Paper Series, No. 7948. doi: 10.3386/w7948
Perkins, E. J. (1991). The separation of commercial and investment banking: the Glass-Steagall Act revisited and reconsidered. Business History Review, 65(02), 442-443. doi: doi:10.2307/3117422
Pickart, C. M., & Eddins, M. J. (2004). Ubiquitin: structures, functions, mechanisms. Biochim Biophys Acta, 1695(1-3), 55-72. doi: 10.1016/j.bbamcr.2004.09.019
Puliga, M., Caldarelli, G., & Battiston, S. (2014). Credit Default Swaps networks and systemic risk. Sci Rep, 4, 6822. doi: 10.1038/srep06822
Reinhart, C. M., & Rogoff, K. S. (2008). Is the 2007 U.S. Sub-Prime Financial Crisis So Different? An International Historical Comparison. National Bureau of Economic Research Working Paper Series, No. 13761. doi: 10.3386/w13761
Schularick, M., & Taylor, A. M. (2009). Credit Booms Gone Bust: Monetary Policy, Leverage Cycles and Financial Crises, 1870-2008. National Bureau of Economic Research Working Paper Series, No. 15512. doi: 10.3386/w15512
Shapiro, J. (2014). Banks to G20: regulation hinders growth. from http://www.afr.com/business/banking-and-finance/financial-services/banks-to-g20-regulation-hinders-growth-20140221-ixqfa
Sherman, M. (2009). A Short History of Financial Deregulation in the United States. Washington, D.C.: Center for Economic Policy and Research.
Sneppen, K., Krishna, S., & Semsey, S. (2010). Simplified models of biological networks. Annu Rev Biophys, 39, 43-59. doi: 10.1146/annurev.biophys.093008.131241
Squartini, T., van Lelyveld, I., & Garlaschelli, D. (2013). Early-warning signals of topological collapse in interbank networks. Sci Rep, 3, 3357. doi: 10.1038/srep03357
Srobar, F. (2004). Feedback loops in the signal paths controlling gene expression. Bioelectrochemistry, 63(1-2), 87-90. doi: 10.1016/j.bioelechem.2003.11.003
Stelling, J., Sauer, U., Szallasi, Z., Doyle, F. J., & Doyle, J. (2004). Robustness of Cellular Functions. Cell, 118(6), 675-685. doi: http://dx.doi.org/10.1016/j.cell.2004.09.008
Terzi, N., & Uluçay, K. (2011). The Role of Credit Default Swaps on Financial Market Stability. Procedia – Social and Behavioral Sciences, 24, 983-990. doi: 10.1016/j.sbspro.2011.09.066
Tett, G. (2010). Fool’s Gold: The Inside Story of J.P. Morgan and How Wall St. Greed Corrupted Its Bold Dream and Created a Financial Catastrophe: Simon and Schuster.
Vitali, S., Glattfelder, J. B., & Battiston, S. (2011). The network of global corporate control. PLoS One, 6(10), e25995. doi: 10.1371/journal.pone.0025995
Wald, C. (2008). Crazy Money. Science, 322(5908), 1624-1626.
Wuchty, S., Ravasz, E., & Barabási, A.-L. (2006). The Architecture of Biological Networks. In T. Deisboeck & J. Y. Kresh (Eds.), Complex Systems Science in Biomedicine (pp. 165-181): Springer US.