Herding and zero-intelligence agents in the order book dynamics of an artificial double auction market

Effects of herding on the order book dynamics of a double auction market is studied by an agent-based model. This is done by comparing results from a zero-intelligence model and a model in which herding effect is implemented by aggregation of agents who take market orders into opinion groups. The number of opinion groups in a simulation step is determined from previous volatilities of the market as different agents compare the price change over different time intervals. Besides confirming that when herding is included the tail of the distribution of volatility is enhanced, we found several new results. First, the autocorrelation time of volatility is much shorter than the memory of most of the agents because limit orders have strong influence on the location of best bid and best ask. Second, from the relation between bid-ask imbalance and price return we find that herding reduces the chance for a small imbalance to produce a large price change. Furthermore, herding tends to decrease spread. This is because herding decreases the chance that a market order changes the size of the spread. Finally, we find that the relation between spread and volatility in our models does not agree with empirical data, this indicates a difference between agents with no strategies and agents in real financial markets.     

Extreme-volatility dynamics in crude oil markets

Based on concepts and methods from statistical physics, we investigate extreme-volatility dynamics in the crude oil markets, using the high-frequency data from 2006 to 2010 and the daily data from 1986 to 2016. The dynamic relaxation of extreme volatilities is described by a power law, whose exponents usually depend on the magnitude of extreme volatilities. In particular, the relaxation before and after extreme volatilities is time-reversal symmetric at the high-frequency time scale, but time-reversal asymmetric at the daily time scale. This time-reversal asymmetry is mainly induced by exogenous events. However, the dynamic relaxation after exogenous events exhibits the same characteristics as that after endogenous events. An interacting herding model both with and without exogenous driving forces could qualitatively describe the extreme-volatility dynamics.     

Punctuated equilibrium and power law in economic dynamics

This work is primarily based on a recently proposed toy model by Thurner et al. (2010) [3] on Schumpeterian economic dynamics (inspired by the idea of economist Joseph Schumpeter [9]). Interestingly, punctuated equilibrium has been shown to emerge from the dynamics. The punctuated equilibrium and Power law are known to be associated with similar kinds of biologically relevant evolutionary models proposed in the past. The occurrence of the Power law is a signature of Self-Organised Criticality (SOC). In our view, power laws can be obtained by controlling the dynamics through incorporating the idea of feedback into the algorithm in some way. The so-called ‘feedback’ was achieved by introducing the idea of fitness and selection processes in the biological evolutionary models. Therefore, we examine the possible emergence of a power law by invoking the concepts of ‘fitness’ and ‘selection’ in the present model of economic evolution.     

Grafting of higher-order correlations of real financial markets into herding models

In this work, we graft the volatility clustering observed in empirical financial time series into the Equiluz and Zimmermann (EZ) model, which was introduced to reproduce the herding behaviors of a financial time series. The original EZ model failed to reproduce the empirically observed power-law exponents of real financial data. The EZ model ordinarily produces a more fat-tailed distribution compared to real data, and a long-range correlation of absolute returns that underlie the volatility clustering. As it is not appropriate to capture the empirically observed correlations in a modified EZ model, we apply a sorting method to incorporate the nonlinear correlation structure of a real financial time series into the generated returns. By doing so, we observe that the slow convergence of distribution of returns is well established for returns generated from the EZ model and its modified version. It is also found that the modified EZ model leads to a less fat-tailed distribution.     

Universal behavior in a generalized model of contagion

Models of contagion arise broadly in both the biological and the social sciences, with applications ranging from the transmission of infectious diseases to the spread of cultural fads. In this Letter, we introduce a general model of contagion which, by explicitly incorporating memory of past exposures to, for example, an infectious agent, rumor, or new product, includes the main features of existing contagion models and interpolates between them. We obtain exact solutions for a simple version of the model, finding that under general conditions only three classes of collective dynamics exist. Furthermore, we find that, for a given length of memory, the class into which a particular system falls is determined by only two parameters. Our model suggests novel measures for assessing the susceptibility of a population to large contagion events, and also a possible strategy for inhibiting or facilitating them.     

相互作用herding模型:记忆与遗忘

相互作用herding 模型仅考虑上一事件对当前经济活动的影响，实际经济活动中经纪人对过去的经济活动是具有深度记忆的，过去各个事件对当前经济活动都应该有影响，但是由于遗忘，它们的影响又各不相同.具有长程记忆的相互作用herding模型认为过去各个事件对当前经济活动的影响随时间是呈指数衰退的，它不仅体现经纪人对历史具有深度的记忆能力，同时也反映出人的遗忘本性.数值计算表明，该模型不仅能重现相互作用herding模型的动力学特性，并且较好地反映真实的经济行为. 关键词： 记忆 遗忘 相互作用herding模型 指数衰退     

Conflict game in evacuation process: A study combining Cellular Automata model

The game-theoretic approach is an essential tool in the research of conflicts of human behaviors. The aim of this study is to research crowd dynamic conflicts during evacuation processes. By combining a conflict game with a Cellular Automata model, the following factors such as rationality, herding effect and conflict cost are taken into the research on frequency of each strategy of evacuees, and evacuation time. Results from Monte Carlo simulations show that (i) in an emergency condition, rationality leads to “vying” behaviors and inhibited “polite” behavior; (ii) high herding causes a crowd of high rationality (especially in normal circumstances) to become more “vying” in behavior; (iii) the high-rationality crowd is shown to spend more evacuation time than a low-rationality crowd in emergency situations. This study provides a new perspective to understand conflicts in evacuation processes as well as the rationality of evacuees.     

Asymmetric volatility, volatility clustering, and herding agents with a borrowing constraint

Recent empirical research has documented asymmetric volatility and volatility clustering in stock markets. We conjecture that a limit of arbitrage due to a borrowing constraint and herding behavior by investors are related to these phenomena. This study conducts simulation analyses on a spin model where borrowing constrained agents imitate their nearest neighbors but switch their strategies to a different one intermittently. We show that herding matters for volatility clustering while a borrowing constraint intensifies the asymmetry of volatility through the herding effect.     

Simulation of crowd dynamics in pedestrian evacuation concerning panic contagion: A cellular automaton approach

The study of the panic evacuation process is of great significance to emergency management. Panic not only causes negative emotions such as irritability and anxiety, but also affects the pedestrians decision-making process, thereby inducing the abnormal crowd behavior. Prompted by the epidemiological SIR model, an extended floor field cellular automaton model was proposed to investigate the pedestrian dynamics under the threat of hazard resulting from the panic contagion. In the model, the conception of panic transmission status (PTS) was put forward to describe pedestrians' behavior who could transmit panic emotions to others. The model also indicated the pedestrian movement was governed by the static and hazard threat floor field. Then rules that panic could influence decision-making process were set up based on the floor field theory. The simulation results show that the stronger the pedestrian panic, the more sensitive pedestrians are to hazards, and the less able to rationally find safe exits. However, when the crowd density is high, the panic contagion has a less impact on the evacuation process of pedestrians. It is also found that when the hazard position is closer to the exit, the panic will propagate for a longer time and have a greater impact on the evacuation. The results also suggest that as the extent of pedestrian's familiarity with the environment increases, pedestrians spend less time to escape from the room and are less sensitive to the hazard. In addition, it is essential to point out that, compared with the impact of panic contagion, the pedestrian's familiarity with environment has a more significant influence on the evacuation.     

The dynamic synthesis of general non-conservative structures from separately identified substructure models

The idea of synthesizing a number of directly identified substructure models is developed. Each substructure is tested separately and a substructure model is determined from time domain test data. Structural modeling concepts significantly affect the substructure identification and substructure coupling. The effects are examined in detail.     

Spatio-temporal dynamics of security investments in an interdependent risk environment

In a globalised world where risks spread through contagion, the decision of an entity to invest in securing its premises from stochastic risks no longer depends solely on its own actions but also on the actions of other interacting entities in the system. This phenomenon is commonly seen in many domains including airline, logistics and computer security and is referred to as Interdependent Security (IDS). An IDS game models this decision problem from a game-theoretic perspective and deals with the behavioural dynamics of risk-reduction investments in such settings. This paper enhances this model and investigates the spatio-temporal aspects of the IDS games. The spatio-temporal dynamics are studied using simple replicator dynamics on a variety of network structures and for various security cost tradeoffs that lead to different Nash equilibria in an IDS game. The simulation results show that the neighbourhood configuration has a greater effect on the IDS game dynamics than network structure. An in-depth empirical analysis of game dynamics is carried out on regular graphs, which leads to the articulation of necessary and sufficient conditions for dominance in IDS games under spatial constraints.     

人体外骨骼控制器硬件设计

为了适应外骨骼机器人的发展需求,设计了一款可以实现外骨骼控制的控制器,该控制器采用以PowerPC芯片为核心,FPGA芯片为辅的硬件设计,文中详细说明了各个模块的设计思想及外围接口电路的设计,由于PowerPC芯片较高的内部集成了多种功能,以及在平台中加入了以FPGA来集中控制各个接口,因此可以实现多个传感器接口信号采集和控制,并使用实时操作系统对各个传感器信号处理和控制。该控制器具有运算速度快,扩展方便,可靠性较高的特点。     

Clustering Effect on the Evolution of Cooperation in a Herding Snowdrift Game

We study the evolution of cooperation for two cluster breaking mechanisms in a herding snowdrift game. The cooperative behavior is observed to be related to the duster size. A negative dependence of the payoff parameter r on cooperative behavior is discovered. For a low r, herding helps promote the cooperation, whereas for a high r, herding tends to prevent cooperative behavior.     

Two-phase phenomenon in linear and non-linear financial instruments

Two-phase phenomenon in financial markets can be described as a herding model. In our research, linear property products, 713 stocks and KOSPI 200 futures, show an out-of-equilibrium phase. Non-linear property financial instruments, KOSPI 200 option, however, have different characteristics depending on their general usage. Especially, as we classify put option into OTM and ITM, a two-phase graph is not noticed in OTM put option which is generally used for hedging in normal market, yet it is dually recognized in ITM put option which is less attractive financial derivatives because of its higher cost. By considering the relationship with call option, herding behavior is distorted in the option market, because put call parity restricts both call and put option which evolve separately.     

Self-organizing Ising model of financial markets

We study a dynamical Ising-like model of agents' opinions (buy or sell) with learning, in which the coupling coefficients are re-assessed continuously in time according to how past external news (time-varying magnetic field) have explained realized market returns. By combining herding, the impact of external news and private information, we find that the stylized facts of financial markets are reproduced only when agents misattribute the success of news to predict return to herding effects, thereby providing positive feedbacks leading to the model functioning close to the Ising critical point.     

Temporal solitonic crystals and non-hermitian informational lattices

Clusters of temporal optical solitons--stable self-localized light pulses preserving their form during propagation--exhibit properties characteristic of that encountered in crystals. Here, we introduce the concept of temporal solitonic information crystals formed by the lattices of optical pulses with variable phases. The proposed general idea offers new approaches to optical coherent transmission technology and can be generalized to dispersion-managed and dissipative solitons as well as scaled to a variety of physical platforms from fiber optics to silicon chips. We discuss the key properties of such dynamic temporal crystals that mathematically correspond to non-hermitian lattices and examine the types of collective mode instabilities determining the lifetime of the soliton train. This transfer of techniques and concepts from solid state physics to information theory promises a new outlook on information storage and transmission.     

Contagion dynamics on adaptive multiplex networks with awareness-dependent rewiring

Over the last few years, the interplay between contagion dynamics of social influences (e.g., human awareness, risk perception, and information dissemination) and biological infections has been extensively investigated within the framework of multiplex networks. The vast majority of existing multiplex network spreading models typically resort to heterogeneous mean-field approximation and microscopic Markov chain approaches. Such approaches usually manifest richer dynamical properties on multiplex networks than those on simplex networks; however, they fall short of a subtle analysis of the variations in connections between nodes of the network and fail to account for the adaptive behavioral changes among individuals in response to epidemic outbreaks. To transcend these limitations, in this paper we develop a highly integrated effective degree approach to modeling epidemic and awareness spreading processes on multiplex networks coupled with awareness-dependent adaptive rewiring. This approach keeps track of the number of nearest neighbors in each state of an individual; consequently, it allows for the integration of changes in local contacts into the multiplex network model. We derive a formula for the threshold condition of contagion outbreak. Also, we provide a lower bound for the threshold parameter to indicate the effect of adaptive rewiring. The threshold analysis is confirmed by extensive simulations. Our results show that awareness-dependent link rewiring plays an important role in enhancing the transmission threshold as well as lowering the epidemic prevalence. Moreover, it is revealed that intensified awareness diffusion in conjunction with enhanced link rewiring makes a greater contribution to disease prevention and control. In addition, the critical phenomenon is observed in the dependence of the epidemic threshold on the awareness diffusion rate, supporting the metacritical point previously reported in literature. This work may shed light on understanding of the interplay between epidemic dynamics and social contagion on adaptive networks.     

SARS疫情分析及对北京疫情走势的预测

在考虑每个SARS病人每日平均传染概率和他的直接传染的平均期限的基础上，建立了一个统计分析模型．分析表明，每个病人可以造成直接感染他人的期限平均在20天左右，这个值在不同地区和不同疫情阶段似乎变化不大．病人的平均每天感染率与社会状况有关，在疫情爆发期较大，在疫情控制期要小很多．北京后期如果控制在香港后期的感染率水平上，则有望在6月上中旬下降到日增几例．然后再经过约一个月，即7月上中旬达到日增0病例，而累积总病例数将达到3100多．但如果北京的新病例下降速度与广东类似的话，则要再多花至少一个月，才能达到上述的效果，且累积总病例数会达到3800左右．     

Behavioural breaks in the heterogeneous agent model: The impact of herding,overconfidence, and market sentiment

The main aim of this work is to incorporate selected findings from behavioural finance into a Heterogeneous Agent Model using the Brock and Hommes (1998) [34] framework. Behavioural patterns are injected into an asset pricing framework through the so-called ‘Break Point Date’, which allows us to examine their direct impact. In particular, we analyse the dynamics of the model around the behavioural break. Price behaviour of 30 Dow Jones Industrial Average constituents covering five particularly turbulent US stock market periods reveals interesting patterns in this aspect. To replicate it, we apply numerical analysis using the Heterogeneous Agent Model extended with the selected findings from behavioural finance: herding, overconfidence, and market sentiment. We show that these behavioural breaks can be well modelled via the Heterogeneous Agent Model framework and they extend the original model considerably. Various modifications lead to significantly different results and model with behavioural breaks is also able to partially replicate price behaviour found in the data during turbulent stock market periods.     

Fundamental factors versus herding in the 2000–2005 US stock market and prediction

We present a general methodology to incorporate fundamental economic factors to the theory of herding developed in our group to describe bubbles and antibubbles. We start from the strong form of rational expectation and derive the general method to incorporate factors in addition to the log-periodic power law (LPPL) signature of herding developed in ours and others’ works. These factors include interest rate, interest spread, historical volatility, implied volatility and exchange rates. Standard statistical AIC and Wilks tests allow us to compare the explanatory power of the different proposed factor models. We find that the historical volatility played the key role before August of 2002. Around October 2002, the interest rate dominated. In the first six months of 2003, the foreign exchange rate became the key factor. Since the end of 2003, all factors have played an increasingly large role. However, the most surprising result is that the best model is the second-order LPPL without any factor. We thus present a scenario for the future evolution of the US stock market based on the extrapolation of the fit of the second-order LPPL formula, which suggests that herding is still the dominating force and that the unraveling of the US stock market antibubble since 2000 is still qualitatively similar to (but quantitatively different from) the Japanese Nikkei case after 1990.