PK|i<)UYYrefs.MYD?+Lev Muchnik Frantisek Slanina Sorin Solomon2003bThe interacting gaps model: reconciling theoretical and numerical approaches to limit-order models232-239 Physica A3301-22Econophysics, Power law, Zipf, Pareto, Limit order12/2003MWe consider the emergence of power-law tails in the returns distribution of limit-order driven markets. We explain a previously observed clash between the theoretical and numerical studies of such models. We introduce a solvable model that interpolates between the previous studies and agrees with each of them in the relevant limit.http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6TVG-49KGVPD-8&_user=856235&_coverDate=12%2F01%2F2003&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000046180&_version=1&_urlVersion=0&_userid=856235&md5=9d5b356ea181fa94e929df5a3e4f3643Khttp://www.levmuchnik.net/Files/Publications/The_Interacting_Gaps_Model.pdf@v Muchnik Sorin Solomon2003ZStatistical Mechanics of Conventional Traders May Lead to Non-Conventional Market Behavior41-47 Phys. Scr.106vAbstract. We describe the main idea and the conceptual architecture of a platform for simulating a large number of asynchronously interacting agents in continuous time. We show how the generic capabilities of the platform apply to the simulation of realistic stock market interactions. A particular example of a very dramatic market event that took place in Financial Times Stock Exchange (FTSE) on September 20, 2002 is used to uncover the parameters characterizing the classical investor types within the market. The simple microscopic rules governing the individual agents behavior are shown to result in a collective market behavior similar to the one of a damped harmonic oscillator. Specifically, the aggregated influence of the fundamentalist traders is formally related to Hooke's law while the behavior of the trend followers corresponds to inertia and viscous friction forces.6http://www.iop.org/EJ/abstract/1402-4896/2003/T106/010http://www.levmuchnik.net/Files/Publications/Statistical_Mechanics_of_Conventional_Traders_May_Lead_to_Non-Conventional_Market_Behavior.pdf?`Aharon Blank Gil Alexandrowicz Lev Muchnik Gil Tidhar Jacob Schneiderman Renu Virmani Erez Golan2004`Miniature self-contained intravascular magnetic resonance (IVMI) probe for clinical applications105-112Magnetic Resonance in Medicine541/NMR, MRI,inside-out ,ex situ, vulnerable plaqueA miniature (1.73 mm in diameter) NMR probe, which contains a magnet and a radiofrequency (RF) coil, is presented. This probe is integrated at the tip of a standard catheter and can be inserted into the human coronary arteries, creating local magnetic fields needed to obtain the NMR signal from the blood vessel walls, without the need for external magnet or RF coils. The basic theory governing the probe performance in terms of signal-to-noise-ratio and contrast parameters is presented, along with measured results from test samples. The NMR signal can be analyzed to obtain tissue contrast parameters such as T1, T2 and the diffusion coefficient, which may be used to detect lipid-rich vulnerable plaques in the coronary arteries. Magn Reson Med 54:105-112, 2005. © 2005 Wiley-Liss, Inc.Xhttp://www3.interscience.wiley.com/cgi-bin/abstract/110541544/ABSTRACT?CRETRY=1&SRETRY=0http://www.levmuchnik.net/Files/Publications/Miniature_Self-Contained_Intravascular_Magnetic_Resonance_Probe_for_Clinical_Application.pdf http://www3.interscience.wiley.com/cgi-bin/fulltext/110541544/PDFSTART?'Lev Muchnik Yoram Louzoun Sorin Solomon2006IAgent Based Simulation Design Principles — Applications to Stock Market183-188XPractical Fruits of Econophysics. Proceedings of the Third Nikkei Econophysics SymposiumaAgent-Based Simulation, Experimental Markets, Artificial Financial Markets, Market Microstructure June 18, 2006We present a novel agent based simulation platform designed for general-purpose modeling in social sciences. Beyond providing convenient environment for modeling, debugging, simulation and analysis, the platform automatically enforces many of the properties inherent to the reality (such as causality and precise timing of events). A unique formalism grants agents with an unprecedented flexibility of actions simultaneously isolating researchers from most of the overhead of the virtual environment maintenance.5http://www.springerlink.com/content/t4676j4576718706/Yhttp://www.LevMuchnik.net/Files/Publications/Agent_Based_Simulation_Design_Principles.pdfR?'Yoram Louzoun Lev Muchnik Sorin Solomon2006dCopying nodes vs. Editing links: the source of the difference between genetic networks and the WWW. 581-588Bioinformatics 225We study two kinds of networks: genetic regulatory networks and the World Wide Web. We systematically test microscopic mechanisms to find the set of such mechanisms that optimally explain each networks' specific properties. In the first case we formulate a model including mainly random unbiased gene duplications and mutations. In the second case, the basic moves are website generation and rapid surf-induced link creation (/destruction). The different types of mechanisms reproduce the appropriate observed network properties. We use those to show that different kinds of networks have strongly system-dependent macroscopic experimental features. The diverging properties result from dissimilar node and link basic dynamics. The main non-uniform properties include the clustering coefficient, small-scale motifs frequency, time correlations, centrality and the connectivity of outgoing links. Some other features are generic such as the large-scale connectivity distribution of incoming links (scale-free) and the network diameter (small-worlds). The common properties are just the general hallmark of autocatalysis (self-enhancing processes), while the specific properties hinge on the specific elementary mechanisms.`http://portal.acm.org/citation.cfm?id=1181568.1181596&coll=&dl=ACM&CFID=15151515&CFTOKEN=6184618 1367-4803Phttp://www.LevMuchnik.net/Files/Publications/Copying_nodes_vs._Editing_links.pdfGX?GKazuko Yamasaki Lev Muchnik Shlomo Havlin Armin Bunde H. Eugene Stanley2005FScaling and memory in volatility return intervals in financial markets 9424-9428 PNAS10226Xeconophysics , fluctuations , extreme values , long-term correlations , long-term memoryFor both stock and currency markets, we study the return intervals {tau} between the daily volatilities of the price changes that are above a certain threshold q. We find that the distribution function Pq({tau}) scales with the mean return interval -{tau} as Pq({tau}) = -{tau}-1f({tau}/-{tau}). The scaling function f(x) is similar in form for all seven stocks and for all seven currency databases analyzed, and f(x) is consistent with a power-law form, f(x) ~ x-{gamma} with {gamma} {approx} 2. We also quantify how the conditional distribution Pq({tau}|{tau}0) depends on the previous return interval {tau}0 and find that small (or large) return intervals are more likely to be followed by small (or large) return intervals. This "clustering" of the volatility return intervals is a previously unrecognized phenomenon that we relate to the long-term correlations known to be present in the volatility.http://www.pnas.org/cgi/content/abstract/0502613102v1 Publications/Scaling_and_memory_in_volatility_return_intervals_in_financial_markets.pdfjhttp://www?GKazuko Yamasaki Lev Muchnik Shlomo Havlin Armin Bunde H. Eugene Stanley2006NScaling and Memory in Return Loss Intervals and Application to Risk Estimation43-51XPractical Fruits of Econophysics. Proceedings of the Third Nikkei Econophysics Symposium>return loss intervals , scaling , universality , value-at-riskWe study the statistics of the return intervals τq between two consecutive return losses below a threshold −q, in various stocks, currencies and commodities. We find the probability distribution function (pdf) of τq scales with the mean return interval τq in a quite universal way, which may enable us to extrapolate rare events from the behavior of more frequent events with better statistics. The functional form of the pdf shows deviation from a simple exponential behavior, suggesting memory effects in losses. The memory shows up strongly in the conditional mean loss return intervals which depend significantly on the previous interval. This dependence can be used to improve the estimate of the risk level.http://www.google.com/url?sa=t&ct=res&cd=1&url=http%3A%2F%2Fwww.springerlink.com%2Findex%2Ft61n57432363x42w.pdf&ei=OZY4Rti-MIqK0gSTr7WODA&usg=AFrqEzf9dx6KbQRsqT_QFEC89qY5p1-A6g&sig2=E-lN64nK6qx-QoEtCkL0iQ9http://www.LevMuchnik.net/Files/Publications/spie2005.pdf ?*Daniel, Gilles Muchnik, Lev Solomon, Sorin2006BTraders imprint themselves by adaptively updating their own avatar27-38Lecture Notes in Economics and Mathematical Systems: Artificial Economics, Agent-Based Methods in Finance, Game Theory and Their Applications564'P. Mathieu, B. Beaufils and O. Brandouy Springer Simulations of artificial stock markets were considered as early as 1964 and multi-agent ones were introduced as early as 1989. Starting the early 90's, collaborations of economists and physicists produced increasingly realistic simulation platforms. Currently, the market stylized facts are easily reproduced and one has now to address the realistic details of the Market Microstructure and of the Traders Behaviour. This calls for new methods and tools capable of bridging smoothly between simulations and experiments in economics. We propose here the following Avatar-Based Method (ABM). The subjects implement and maintain their Avatars (programs encoding their personal decision making procedures) on NatLab, a market simulation platform. Once these procedures are fed in a computer edible format, they can be operationally used as such without the need for belabouring, interpreting or conceptualising them. Thus ABM short-circuits the usual behavioural economics experiments that search for the psychological mechanisms underlying the subjects behaviour. Finally, ABM maintains a level of objectivity close to the classical behaviourism while extending its scope to subjects' decision making mechanisms. We report on experiments where Avatars designed and maintained by humans from different backgrounds (including real traders) compete in a continuous double-auction market. We hope this unbiased way of capturing the adaptive evolution of real subjects behaviour may lead to a new kind of behavioural economics experiments with a high degree of reliability, analysability and reproducibility. Comment: 12 pages, 4 figures, draft of a paper submitted to Artificial Economics 2005, September 15-16, Lille, FranceBhttp://www.citebase.org/abstract?id=oai%3AarXiv.org%3Acs%2F0509017 3-540-28578-4http://www.LevMuchnik.net/Files/Publications/TradersImprintThemselves.pdf http://www.citebase.org/fulltext?format=application%2Fpdf&identifier=oai%3AarXiv.org%3Acs%2F0509017? Jacob Schneiderman, Robert L. Wilensky Assaf Weiss Eitzek Samouha Lev Muchnik Malca Chen-Zion Mordechay Ilovitch Erez Golan, MSC Aharon Blank Moshe Flugelman Yosef Rozenman Renu Virmani2004Diagnosis of Thin-Cap Fibroatheromas by a Self-Contained Intravascular Magnetic Resonance Imaging Probe in Ex Vivo Human Aortas and In Situ Coronary Arteries 1961-1969-Journal of the American College of Cardiology4512 0735-1097Xhttp://www.levmuchnik.net/Files/Publications/Diagnosis_of_Thin_Fibrous_Cap_Atheromas.pdfg?  Lev Muchnik2003Simulating emergence and complex collective dynamics in the stock markets. Master’s thesis, Israel, 2003. Available at http://www.complexity-research.org/natlab200Department of PhysicsMasters*The Hebrew University of Jerusalem, Israel Sorin Solomon7http://www.levmuchnik.net/Files/Publications/Thesis.pdfDw? Lev Muchnik Sorin Solomon2006MMarkov Nets and the NatLab platform; Application to Continuous Double AuctionNew Economic WindowsBerlinSpringer-VerlagIn describing dynamics of classical bodies one uses systems of differential equations (Newton laws). Increasing the number of interacting bodies requires finer time scales and heavier computations. Thus one often takes a statistical approach (e.g. Statistical Mechanics, Markov Chains, Monte Carlo Simulations) which sacrifices the details of the event-by-event causality. The main assumption is that each event is determined only by events immediately preceding it rather than events in the arbitrary past. Moreover, time is often divided in slices and the various cause and effect events are assumed to take place in accordance with this arbitrary slicing. The dynamics of certain economic systems can be expressed similarly. However, in many economic systems, the dynamics is dominated by specific events and specific reactions of the agents to those events. Thus, to keep the model meaningful, causality and in particular the correct ordering of events has to be preserved rigorously down to the lowest time scale. We introduce the concept of Markov Nets (MN), which allows one to represent exactly the causal structure of events in natural systems composed of many interacting agents. The Markov Nets preserve the exclusive dependence of an effect event on the event directly causing it but makes no assumption on the time lapse separating them. Moreover, in a Markov Net the possibility exists that an event is affected if another event happens in the meantime between its causation and its expected occurrence. We present a simulation platform (NatLab) that uses the MN formalism to make simulations that preserve exactly the causal timing of events without paying an impossible computational cost. Salzano, M. <http://www.LevMuchnik.net/Files/Publications/Markov-Nets.pdf? 4Lev Muchnik Royi Itschak Sorin Solomon Yoram Louzoun2007JSelf-emergence of knowledge trees: Extraction of the Wikipedia hierarchies016106 Phys. Rev. E76The rapid accumulation of knowledge and the recent emergence of new dynamic and practically unmoderated information repositories have rendered the classical concept of the hierarchal knowledge structure irrelevant and impossible to impose manually. This led to modern methods of data location, such as browsing or searching, which conceal the underlying information structure. We here propose new methods designed to automatically construct a hierarchy from a network of related terms. We apply these methods to Wikipedia and compare the hierarchy obtained from the article network to the complementary acyclic category layer of the Wikipedia and show an excellent fit. We verify our methods in two networks with no apriori hierarchy (the E. coli genetic regulatory network and the C. Elegans neural network) and a network of function libraries of modern computer operating systems that are intrinsically hierarchical and reproduce a known functional order.,http://link.aps.org/abstract/PRE/v76/e016106Rhttp://www.levmuchnik.net/Files/Publications/Self_Emergence_of_Knowledge_Trees.pdfJ,? %Lev Muchnik Armin Bunde Shlomo Havlin2009<Long term memory in extreme returns of financial time series 4145-4150 Physica A38819It is well known that while daily price returns of financial markets are uncorrelated, their absolute values (‘volatility’) are long-term correlated. Here we provide evidence that certain subsequences of the returns themselves also exhibit long-term memory. These subsequences consist of maxima (or minima) of returns in consecutive time windows of R days. Our analysis shows that for both stocks and currency exchange rates, long-term correlations are significant for R≥4. We argue that this long-term memory which is similar to that observed in volatility clustering sheds furF?YRoyi Itzhack Lev Muchnik Tom Erez Lea Tsaban Jacob Goldenberg Sorin Solomon Yoram Louzoun2009MDiagonal attachment: The Quest for the Mechanism Underlying Network Evolution Submittedv Muchnik Frantisek Slanina Sorin Solomon2003bThe interacting gaps model: reconciling theoretical and numerical approaches to limit-order models232-239 Physica A3301-22Econophysics, Power law, Zipf, Pareto, Limit order12/2003MWe consider the emergence of power-law tails in the returns distribution of limit-order driven markets. We explain a previously observed clash between the theoretical and numerical studies of such models. We introduce a solvable model that interpolates between the previous studies and agrees with each of them in the relevant limit.http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6TVG-49KGVPD-8&_user=856235&_coverDate=12%2F01%2F2003&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000046180&_version=1&_urlVersion=0&_userid=856235&md5=9d5b356ea181fa94e929df5a3e4f3643Khttp://www.levmuchnik.net/Files/Publications/The_Interacting_Gaps_Model.pdf?Lev Muchnik Sorin Solomon2003ZStatistical Mechanics of Conventional Traders May Lead to Non-Conventional Market Behavior41-47 Phys. Scr.106vAbstract. We describe the main idea and the conceptual architecture of a platform for simulating a large number of asynchronously interacting agents in continuous time. We show how the generic capabilities of the platform apply to the simulation of realistic stock market interactions. A particular example of a very dramatic market event that took place in Financial Times Stock Exchange (FTSE) on September 20, 2002 is used to uncover the parameters characterizing the classical investor types within the market. The simple microscopic rules governing the individual agents behavior are shown to result in a collective market behavior similar to the one of a damped harmonic oscillator. Specifically, the aggregated influence of the fundamentalist traders is formally related to Hooke's law while the behavior of the trend followers corresponds to inertia and viscous friction forces.6http://www.iop.org/EJ/abstract/1402-4896/2003/T106/010http://www.levmuchnik.net/Files/Publications/Statistical_Mechanics_of_Conventional_Traders_May_Lead_to_Non-Conventional_Market_Behavior.pdfD?!1M. Shatner, L. Muchnik, M. Leshno, and S. Solomon2000NA continuous time asynchronous model of the stock market; beyond the LLS model0Economic Dynamics from the Physics Point of View!Physikzentrum Bad Honnef, Germany ther insight on price dynamics that might be used for risk estimation./http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6TVG-4WGF124-1&_user=142623&_coverDate=10/01/2009&_rdoc=1&_fmt=high&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1241183446&_rerunOrigin=google&_acct=C000000333&_version=1&_urlVersion=0&_userid=142623&md5=7966a9bf57d7e4e7564ef6e6469f024einternal-pdf://Long_term_memory_in_extreme_returns_of_financial_time_series-0484431119/Long_term_memory_in_extreme_returns_of_financial_time_series.pdfF?lMaksim Kitsak Lazaros K. Gallos Shlomo Havlin Fredrik Liljeros Lev Muchnik H. Eugene Stanley Hernan A. Makse20105Identifying influential spreaders in complex networks SubmittedNetworks portray a multitude of interactions through which people meet, ideas are spread, and infectious diseases propagate within a society. Identifying the most efficient "spreaders" in a network is an important step to optimize the use of available resources and ensure the more efficient spread of information. Here we show that, in contrast to common belief, the most influential spreaders in a social network do not correspond to the best connected people or to the most central people high betweenness centrality. Instead, we find: i The most efficient spreaders are those located within the core of the network as identified by the k-shell decomposition analysis. ii When multiple spreaders are considered simultaneously, the distance between them becomes the crucial parameter that determines the extend of the spreading. Furthermore, we find that-- in the case of infections that do not confer immunity on recovered individuals-- the infection persists in the high k-shell layers of the network under conditions where hubs may not be able to preserve the infection. Our analysis provides a plausible route for an optimal design of efficient dissemination strategies.http://arxiv.org/abs/1001.5285internal-pdf://Identifying_influential_spreaders_in_complex_networks-0535090959/Identifying_influential_spreaders_in_complex_networks.pdfU?(Sinan Aral Lev Muchnik Arun Sundararajan2009\Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks 21544-21549PNAS10651yNode characteristics and behaviors are often correlated with the structure of social networks over time. While evidence of this type of assortative mixing and temporal clustering of behaviors among linked nodes is used to support claims of peer influence and social contagion in networks, homophily may also explain such evidence. Here we develop a dynamic matched sample estimation framework to distinguish influence and homophily effects in dynamic networks, and we apply this framework to a global instant messaging network of 27.4 million users, using data on the day-by-day adoption of a mobile service application and users' longitudinal behavioral, demographic, and geographic data. We find that previous methods overestimate peer influence in product adoption decisions in this network by 300–700%, and that homophily explains >50% of the perceived behavioral contagion. These findings and methods are essential to both our understanding of the mechanisms that drive contagions in networks and our knowledge of how to propagate or combat them in domains as diverse as epidemiology, marketing, development economics, and public health. .http://www.pnas.org/content/106/51/21544.short10.1073/pnas.0908800106internal-pdf://SM2-0417934095/SM2.avi internal-pdf://SM1-1474898703/SM1.avi internal-pdf://0908800106SI-2263427855/0908800106SI.pdf internal-pdf://PNAS-2009-Aral-21544-9-3303615247/PNAS-2009-Aral-21544-9.pdf `.LevMuchnik.net/Files/Publications/0502613102v1.pdf http://www.pnas.org/cgi/reprint/0502613102v1PK/6I/**refs.FRM 0B< !// !HPRIMARYyearIndex 6ByP/) idreference_type text_stylesauthoryear title pages secondary_title volume numbernumber_of_volumessecondary_authorplace_published publishersubsidiary_authoredition keywords type_of_workdate2)  abstractlabelurltertiary_titletertiary_author notes isbn custom_1 custom_2 custom_3 custom_4alternate_titleaccession_number call_number short_title custom_5 custom_6sectionoriginal_publicationH) reprint_editionreviewed_itemauthor_addressimagecaption custom_7 electronic_resource_number link_to_pdf translated_author translated_titlename_of_databasedatabase_providerresearch_notes language access_datelast_modified_date !! H!H!H! (H! 3H! >H! IH! TH!_H!jH!uH! H!H!H! H! H!H! H!H!H!H!H! H! H! H! H! %H! 0H!;H!FH! QH! \H! gH! rH!}H!H!H!H!H!H!H! H! H! H! H! H!H! H!H! "H! -H!8H!idreference_typetext_stylesauthoryeartitlepagessecondary_titlevolumenumbernumber_of_volumessecondary_authorplace_publishedpublishersubsidiary_authoreditionkeywordstype_of_workdateabstractlabelurltertiary_titletertiary_authornotesisbncustom_1custom_2custom_3custom_4alternate_titleaccession_numbercall_numbershort_titlecustom_5custom_6sectionoriginal_publicationreprint_editionreviewed_itemauthor_addressimagecaptioncustom_7electronic_resource_numberlink_to_pdftranslated_authortranslated_titlename_of_databasedatabase_providerresearch_noteslanguageaccess_datelast_modified_datePK|i<)UYYrefs.MYDPK/6I/**BYrefs.FRMPKl@