Understanding Financial Market States Using an Artificial Double Auction Market

The ultimate value of theories describing the fundamental mechanisms behind asset prices in financial systems is reflected in the capacity of such theories to understand these systems. Although the models that explain the various states of financial markets offer substantial evidence from the fields of finance, mathematics, and even physics, previous theories that attempt to address the complexities of financial markets in full have been inadequate. We propose an artificial double auction market as an agent-based model to study the origin of complex states in financial markets by characterizing important parameters with an investment strategy that can cover the dynamics of the financial market. The investment strategies of chartist traders in response to new market information should reduce market stability based on the price fluctuations of risky assets. However, fundamentalist traders strategically submit orders based on fundamental value and, thereby stabilize the market. We construct a continuous double auction market and find that the market is controlled by the proportion of chartists, P_c. We show that mimicking the real state of financial markets, which emerges in real financial systems, is given within the range P_c = 0.40 to P_c = 0.85; however, we show that mimicking the efficient market hypothesis state can be generated with values less than P_c = 0.40. In particular, we observe that mimicking a market collapse state is created with values greater than P_c = 0.85, at which point a liquidity shortage occurs, and the phase transition behavior is described at P_c = 0.85.     

Emergence of Self-Reproduction in Cooperative Chemical Evolution of Prebiological Molecules

The paper presents a model of coevolution of short peptides (P) and short oligonucleotides (N) at an early stage of chemical evolution leading to the origin of life. The model describes polymerization of both P and N types of molecules on mineral surfaces in aqueous solution at moderate temperatures. It is assumed that amino acid and nucleotide monomers were available in a prebiotic milieu, that periodic variation in environmental conditions between dry/warm and wet/cool took place and that energy sources were available for the polymerization. An artificial chemistry approach in combination with agent-based modeling was used to explore chemical evolution from an initially random mixture of monomers. It was assumed that the oligonucleotides could serve as templates for self-replication and for translation of peptide compositional sequences, and that certain peptides could serve as weak catalysts. Important features of the model are the short lengths of the peptide and oligonucleotide molecules that prevent an error catastrophe caused by copying errors and a finite diffusion rate of the molecules on a mineral surface that prevents excessive development of parasitism. The result of the simulation was the emergence of self-replicating molecular systems consisting of peptide catalysts and oligonucleotide templates. In addition, a smaller but significant number of molecules with alternative compositions also survived due to imprecise reproduction and translation of templates providing variability for further evolution. In a more general context, the model describes not only peptide-oligonucleotide molecular systems, but any molecular system containing two types of polymer molecules: one of which serves as templates and the other as catalysts.The presented coevolutionary system suggests a possible direction towards finding the origin of molecular functionality in a prebiotic environment.     

Emergence of Connectivity Motifs in Networks of Model Neurons with Short- and Long-Term Plastic Synapses

Recent experimental data from the rodent cerebral cortex and olfactory bulb indicate that specific connectivity motifs are correlated with short-term dynamics of excitatory synaptic transmission. It was observed that neurons with short-term facilitating synapses form predominantly reciprocal pairwise connections, while neurons with short-term depressing synapses form predominantly unidirectional pairwise connections. The cause of these structural differences in excitatory synaptic microcircuits is unknown. We show that these connectivity motifs emerge in networks of model neurons, from the interactions between short-term synaptic dynamics (SD) and long-term spike-timing dependent plasticity (STDP). While the impact of STDP on SD was shown in simultaneous neuronal pair recordings in vitro, the mutual interactions between STDP and SD in large networks are still the subject of intense research. Our approach combines an SD phenomenological model with an STDP model that faithfully captures long-term plasticity dependence on both spike times and frequency. As a proof of concept, we first simulate and analyze recurrent networks of spiking neurons with random initial connection efficacies and where synapses are either all short-term facilitating or all depressing. For identical external inputs to the network, and as a direct consequence of internally generated activity, we find that networks with depressing synapses evolve unidirectional connectivity motifs, while networks with facilitating synapses evolve reciprocal connectivity motifs. We then show that the same results hold for heterogeneous networks, including both facilitating and depressing synapses. This does not contradict a recent theory that proposes that motifs are shaped by external inputs, but rather complements it by examining the role of both the external inputs and the internally generated network activity. Our study highlights the conditions under which SD-STDP might explain the correlation between facilitation and reciprocal connectivity motifs, as well as between depression and unidirectional motifs.     

Decrypting Financial Markets through E-Joint Attention Efforts: On-Line Adaptive Networks of Investors in Periods of Market Uncertainty

This paper looks at 800,000 messages on the Unicredit stock, exchanged by 7,500 investors in the Finanzaonline.com forum, between 2005 and 2012 and measured collective interpretations of stock market trends. We examined the correlation patterns between market uncertainty, bad news and investors'' network structure by measuring the investors'' communication patterns. Our results showed that the investors'' network reacted to market trends in different ways: While less turbulent market phases implied less communication, higher market volatility generated more complex communication patterns. While the information content of messages was less technical in situations of uncertainty, bad news caused more informative messages only when market volatility was lower. This meant that bad news had a different impact on network behaviour, depending on market uncertainty. By measuring the investors'' expertise, we found that their behaviour could help predict changes in daily stock returns. We also found that expert investors were more influential in communication processes during high volatility market phases, whereas they had less influence on the real-time forum''s reaction after bad news. Our findings confirm the crucial role of e-communication platforms. However, they also show the need to reconsider the fragility of these collective intelligence systems when under external shocks.     

Emergence of asymmetry in evolution

We investigate symmetry-breaking bifurcation patterns in evolution in the framework of adaptive dynamics (AD). We define weak and strong symmetry. The former applies for populations where only the simultaneous reflection of all individuals is an invariant transformation. The symmetry is strong in populations where reflection of some, but not all, individuals leaves the situation unchanged. We show that in case of weak symmetry evolutionary branching can lead to the emergence of two asymmetric variants, which are mirror images of each other, and the loss of the symmetric ancestor. We also show that in case of strong symmetry, evolutionary branching can occur into a symmetric and an asymmetric variant, both of which survive. The latter, asymmetric branching differs from the generic branching patterns of AD, which is always symmetric. We discuss biological examples for weak and strong symmetries and a specific model producing the new kind of branching.     

Emergence of Multiplex Communities in Collaboration Networks

Community structures in collaboration networks reflect the natural tendency of individuals to organize their work in groups in order to better achieve common goals. In most of the cases, individuals exploit their connections to introduce themselves to new areas of interests, giving rise to multifaceted collaborations which span different fields. In this paper, we analyse collaborations in science and among movie actors as multiplex networks, where the layers represent respectively research topics and movie genres, and we show that communities indeed coexist and overlap at the different layers of such systems. We then propose a model to grow multiplex networks based on two mechanisms of intra and inter-layer triadic closure which mimic the real processes by which collaborations evolve. We show that our model is able to explain the multiplex community structure observed empirically, and we infer the strength of the two underlying social mechanisms from real-world systems. Being also able to correctly reproduce the values of intra-layer and inter-layer assortativity correlations, the model contributes to a better understanding of the principles driving the evolution of social networks.     

Emergence of Blind Areas in Information Spreading

Recently, contagion-based (disease, information, etc.) spreading on social networks has been extensively studied. In this paper, other than traditional full interaction, we propose a partial interaction based spreading model, considering that the informed individuals would transmit information to only a certain fraction of their neighbors due to the transmission ability in real-world social networks. Simulation results on three representative networks (BA, ER, WS) indicate that the spreading efficiency is highly correlated with the network heterogeneity. In addition, a special phenomenon, namely Information Blind Areas where the network is separated by several information-unreachable clusters, will emerge from the spreading process. Furthermore, we also find that the size distribution of such information blind areas obeys power-law-like distribution, which has very similar exponent with that of site percolation. Detailed analyses show that the critical value is decreasing along with the network heterogeneity for the spreading process, which is complete the contrary to that of random selection. Moreover, the critical value in the latter process is also larger than that of the former for the same network. Those findings might shed some lights in in-depth understanding the effect of network properties on information spreading.     

Emergence of individual recognition in young macaques

Few studies have addressed the development of nonhuman primate infants' responses to conspecific vocalizations. Previous studies showed that the appropriate response to alarm, intergroup and long-distance contact calls emerged at about 6 months of age. It remained unclear whether this age constitutes a watershed in terms of infants' sociocognitive development or whether it was due to the types of stimuli used in the experiments. I therefore examined the development of infant Barbary macaque, Macaca sylvanus, responses to maternal calls, under the assumption that recognition of the mother is one of the tasks that infants should master as early as possible. I presented infants of different age categories with short bouts of screams recorded from their mothers or another female of the same social group. Experiments on yearlings confirmed the suitability of the experimental approach: yearlings responded significantly more strongly to maternal calls than to calls from unrelated females. Infants were tested at 4, 10 and 16 weeks of age. In the youngest age group, they failed to respond to the playbacks, whereas from 10 weeks of age on they responded significantly more strongly to maternal calls, suggesting that by this age they recognized their mothers by voice. These results suggest that the developmental trajectories in the domain of comprehension learning may be flexible, in the sense that infant responses may depend on the salience of, and the exposure to, the call type under study. The experiments also show that screams may transmit individual-specific characteristics that are perceptually salient to the listeners.     

Evaluation of Long-Term Impacts in LCA

When looking at a product’s life cycle, emissions and resource uses, as well as the resulting impacts, usually occur at different points in time. For instance, construction materials are often ‘stored’ in buildings for many decades before they are recycled or disposed of. The goal of the LCA Discussion Forum 22 was to present and discuss arguments pro and contra a temporally differentiated weighting of impacts. The discussion forum started with three talks that illustrated the importance of temporal aspects in LCI and LCIA. The following two presentations discussed the economical principles of discounting, the adequacy of this concept within LCA, and the ethical questions involved. After one further short presentation, three groups were formed that discussed questions about temporally-differentiated weighting, and consequences for LCI as well as LCIA (damage assessment and final weighting). The discussion forum ended with the following conclusions: (a) long-term impacts should be considered in LCA, and (b) long-term emissions should be inventoried separately from short-term emissions. There was no consensus on whether short-term and long-term impacts should be weighted equally. Some prefer to weigh short-term emissions higher, because they are considered to be closer. Consistent and approved forecasts should be used when considering future changes in environmental conditions in LCI and LCIA.     

Emergence of complexity in evolving niche-model food webs

We have analysed mechanisms that promote the emergence of complex structures in evolving model food webs. The niche model is used to determine predator-prey relationships. Complexity is measured by species richness as well as trophic level structure and link density. Adaptive dynamics that allow predators to concentrate on the prey species they are best adapted to lead to a strong increase in species number but have only a small effect on the number and relative occupancy of trophic levels. The density of active links also remains small but a high number of potential links allows the network to adjust to changes in the species composition (emergence and extinction of species). Incorporating effects of body size on individual metabolism leads to a more complex trophic level structure: both the maximum and the average trophic level increase. So does the density of active links. Taking body size effects into consideration does not have a measurable influence on species richness. If species are allowed to adjust their foraging behaviour, the complexity of the evolving networks can also be influenced by the size of the external resources. The larger the resources, the larger and more complex is the food web it can sustain. Body size effects and increasing resources do not change size and the simple structure of the evolving networks if adaptive foraging is prohibited. This leads to the conclusion that in the framework of the niche model adaptive foraging is a necessary but not sufficient condition for the emergence of complex networks. It is found that despite the stabilising effect of foraging adaptation the system displays elements of self-organised critical behaviour.     

Emergence of structural patterns in neutral trophic networks

Interaction networks are central elements of ecological systems and have very complex structures. Historically, much effort has focused on niche-mediated processes to explain these structures, while an emerging consensus posits that both niche and neutral mechanisms simultaneously shape many features of ecological communities. However, the study of interaction networks still lacks a comprehensive neutral theory. Here we present a neutral model of predator-prey interactions and analyze the structural characteristics of the simulated networks. We find that connectance values (complexity) and complexity-diversity relationships of neutral networks are close to those observed in empirical bipartite networks. High nestedness and low modularity values observed in neutral networks fall in the range of those from empirical antagonist bipartite networks. Our results suggest that, as an alternative to niche-mediated processes that induce incompatibility between species ("niche forbidden links"), neutral processes create "neutral forbidden links" due to uneven species abundance distributions and the low probability of interaction between rare species. Neutral trophic networks must be seen as the missing endpoint of a continuum from niche to purely stochastic approaches of community organization.     

Henipaviruses: Gaps in the Knowledge of Emergence

Over the past 10 years many new viruses have been identified in Australia and the Asian region. The viruses have been isolated from, and/or identified in, a range of animals; some of these viruses are of veterinary and medical importance while others are new threats to biodiversity. Of these viruses, Hendra and Nipah viruses have emerged as significant zoonotic agents belonging to the family Paramyxoviridae and genus Henipavirus. These agents cause fatalities in a range of animals including horses, pigs, and humans. Both viruses have been isolated from flying foxes (genus Pteropus; suborder Megachiroptera), which are accepted as their natural hosts. Although some research has been undertaken on the viruses and their hosts, little is known about how these viruses emerge. We have attempted to investigate the current knowledge of the bat-Henipavirus ecology by discussing the range of bat viruses that exist (viral assemblage), the significance of evolving viruses, possible functional role(s) of viruses, the ecology of viruses and their hosts, and identifying possible drivers (selection pressures) that may culminate with the overlap of new potential viral hosts thereby facilitating the replication of fit viral populations in a new host–virus continuum(s). By undertaking such an analysis, we have attempted to identify key questions, which should be investigated if the factors involved in driving Henipavirus emergence are to be understood.     

Emergence of cancer stem cells in hepatocellular carcinoma

Liver cancer represents the second most deadly human malignancy. The major histological subtype called hepatocellular carcinoma (HCC) arises by chronic inflammation‐triggered regenerative responses of normally quiescent hepatocytes and progenitors, respectively. Such regenerative stress accelerates the accumulation of genetic and epigenetic changes (Yamashita & Wang, 2013 ), while detailed mechanisms remain uncertain. In this issue of The EMBO Journal, Nikolaou et al present a novel HCC model that facilitates both isolation and molecular characterization of self‐renewing, HCC‐propagating cancer stem cells that could instruct future interventions (Nikolaou et al, 2014 ).