Introduction of the Social Thermodynamics Applied Research done by the end of 2009 to mobile operators. Key diference with Neuronal Networks which require a huge amount of resources and personal data, SThAR only requires 4 fields per node.

1. therevolutionof SOCIAL THERMODYNAMICS IN BUSINESS APPLICATIONS A new model to predict social changes…

2. What these events have in common ? Electrical & telephone bills, stock prices, frauds & deaths rates The Percolation theory and the nuclear multi-fragmentation The abundances of genes in various organisms and tissues The churn distribution in a mobile network operator The density distribution of votes in political elections The density distribution of urban agglomerations The distribution of firm-sizes all over the world The frequency of words in natural languages The scientific collaboration network The total number of cites of physics The Linux packages links The Internet traffic… … Are social events determined by universal laws ?

4. Regularity in the words frequency in natural languages and urban agglomeration was empirically already observed, about 100 years ago: The Zipf’s Law.

6. Zipf’s Law Zipf's law, an empirical law formulated using mathematical statistics, refers to the fact that many types of data studied in the physical and social sciences can be approximated with a Zipfian distribution, one of a family of related discrete power law probability distributions. Up to now, it was not known why Zipf's law holds for most languages. However, it may be partially explained by the statistical analysis of randomly-generated texts. If the natural log of some data are normally distributed, the data follow the log-normal distribution. This distribution is useful when random influences have an effect that is multiplicative rather than additive. . Much of his effort could explain properties of the Internet , distribution of income within nations, and many other collections of data

7. Benford’s Law Benford's law, also called the first-digit law, states that in lists of numbers from many (but not all) real-life sources of data, the leading digit is distributed in a specific, non-uniform way. According to this law, the first digit is 1 almost one third of the time, and larger digits occur as the leading digit with lower and lower frequency, to the point where 9 as a first digit occurs less than one time in twenty. This distribution of first digits arises logically whenever a set of values is distributed logarithmically. Real-world measurements are often distributed logarithmically (or equally, the logarithm of the measurements is distributed uniformly). A logarithmic scale bar. Picking a random x position on this number line, roughly 30% of the time the first digit of the number will be 1 (the widest band of each power of ten).

8. Benford’s Law This counter-intuitive result has been found to apply to a wide variety of data sets. The results also hold regardless of the base in which the numbers are expressed, although the exact proportions change. It has been argued that Benford's law is a special case of Zipf's law. This special connection between these two laws can be explained by the fact that they both originate from the same scale invariant functional relation from statistical physics and critical phenomena

9. Fisher Information In mathematical statistics and information theory, the Fisher Information (sometimes simply called Information) is the variance of the score. Its role in the asymptotic theory of maximum-likelihood estimation was emphasized by statistician R.A. Fisher The Fisher information is a way of measuring the amount of information that an observable random variable X carries about an unknown parameter θ upon which the likelihood function of θ, L(θ) = f(X;θ), depends. Fisher information is widely used in optimal experimental design. Because of the reciprocity of estimator-variance and Fisher information, minimizing the variance corresponds to maximizing the information. When the linear statistical model has several parameters, the mean of the parameter-estimator is a vector and its variance is a matrix. The inverse matrix of the variance-matrix is called the "information matrix". Using statistical theory, statisticians compress the information-matrix using real-valued summary statistics; being real-valued functions, these "information criteria" can be maximized.

10. Dunbar’snumber Dunbar's number is a theoretical cognitive limit to the number of people with whom one can maintain stable social relationships. These are relationships in which an individual knows who each person is, and how each person relates to every other person.[1] Proponents assert that numbers larger than this generally require more restricted rules, laws, and enforced norms to maintain a stable, cohesive group. No precise value has been proposed for Dunbar's number, but a commonly cited approximation is 150. Dunbar's number was first proposed by British anthropologist Robin Dunbar, who theorized that "this limit is a direct function of relative neocortex size, and that this in turn limits group size ... the limit imposed by neocortical processing capacity is simply on the number of individuals with whom a stable inter-personal relationship can be maintained." On the periphery, the number also includes past colleagues such as high school friends with whom a person would want to reacquaint themselves if they met again

11. Sixdegrees of separation Six degrees of separation (also referred to as the "Human Web") refers to the idea that, if a person is one step away from each person they know and two steps away from each person who is known by one of the people they know, then everyone is at most six steps away from any other person on Earth. It was popularised by a play written by John Guare. Kevin Bacon Game: The game "Six Degrees of Kevin Bacon" was invented as a play on the concept: the goal is to link any actor to Kevin Bacon through no more than six connections, where two actors are connected if they have appeared in a movie together.

12. Confidential and Copyrighted … CAN SOCIAL CHANGES BE PREDICTABLE THROUGH A WHOLE INNOVATIVE MODEL ?

17. Like in the result of the Brazilian elections…

19. Like in the result of the Brazilian elections……

20. and in the population distribution in towns in different countries !!…In fact, this regularity is what The Zipf’s Law had confirmed for many years.

22. Distribution of firmsizes

24. Populationnumber

25. Stock Prices…

26. Similar regularities have been detected in many other social events… BUT … NOBODY HAD BEEN ABLE TO EXPLAIN HOW or WHY THESE REGULARITIES HAPPENED BASED ON SCIENTIFIC PRINCIPLES… …AND THERE WAS NEVER A THEORY AVAILABLE TO EXPLAIN IT !! Up to now !! ...

31. Benford’s Law

32. Dunbar’s Number

33. Six degrees of separation

34. “Competitiveness”Explains both Zipf’s Law and Benford’s Law: they naturally emerge when the correct symmetry and variables are introduced in the Information Principle (“Zipf's Law from a Fisher variational-principle”, http://arxiv.org/abs/0908.0501). Confirms the analogy between the properties of Social Systems and the Thermodynamics of Gases and Liquids through the “Scale-Free Ideal Gas” (SFIG): Therefore, the Zipf’s Law is so universal as the Universal Gas Law, as they rise from the same principle, but with different symmetries (“Fisher-information and the thermodynamics of scale-invariant systems”, respectively, http://arxiv.org/abs/0908.0504).

36. Benford’s Law

37. Dunbar’s Number

38. Six degrees of separation

40. Prediction of the min. average distance between any 2 people on the Earth, known as the “Six degrees of Separation”, is a conseq. of Dunbar’s Number. Thanks to Social Thermodynamics, those properties that had been previously detected, only empirically, now have a valid scientific explanation and a model which can be applied to any event.

43. Benford’s Law

44. Dunbar’s Number

45. Six degrees of separation

46. “Competitiveness”As regularityexists, and the theoretic framework has been created, results can bemodeled and, therefore, predicted !

47. benefits of SOCIAL THERMODYNAMICSappliCATIONto social networks

48. Applications

49. Benefits appliedtoorganizations

50. Solution areas

51. Whowe areCOMPANY OVERVIEW

52. Company Overview About SThAR SThAR is the developer and world’s leader in the application of Social Thermodynamics Universal Laws to real business needs, which can provide revolutionary solutions, under a whole new scientific methodology. Vision Through the analysis of network properties and the use of recently discovered physical principles, SThAR will help public and private enterprises to predict and model social interactions for multiple purposes, delivering an unsuspected powerful tool to explain social events and gain a dramatic competitive advantage: Identifying the network Ebullition Points and, therefore, the best and most cost-effective Dissemination Strategies(e.g., for viral marketing purposes). Detecting the Achilles’ Heels and most risky areas susceptible to be threatened leading to the weakening/destruction of the network (for churn reduction, detection of mobile viruses spread, etc) Anticipating social changes and predicting future results thanks to a new theoretical framework, rather than using traditional empirical approaches.

53. Why is SThAR revolutionary ? What makes SThAR unique: A disruptive and whole new approach: Versus more than 100 years of conventional empirical based models With a mathematic model that, instead of using well known statistics methods, explains regularities through physical principles and can predict next ones. Scientific foundation and recognition SThAR scientists are the creators of the applied Social Thermodynamics . Recognition of the scientific international community. Correlating Operator’s and Environmental Networks For a more comprehensive analysis, we can correlate Carrier’s data with social environment behavior, dramatically enhancing the power and accuracy of the combined prediction results.

54. Howweworkmethodology

55. Social ThermodynamicsapplicationMethodology Project Methodology. 5 Phases:

56. Methodology 1. Data Collection (only 4 required fields)

57. Methodology 2. Network Typification

58. Methodology 3. Environmental Info Correlation

59. Methodology 4. Propagation and Network Behaviour Simulation

60. Methodology 5. Predictive Modeling and Automation

61. Application of social themodynamicsbusiness case: DRAMATICALLY IMPROVED CHURN ANALYSIS FOR a Mobile operator

62. Application of Social ThermodynamicsBusiness Case: A Mobile Operator 1 single model => 5 applications: Disruptive Churn Analysis Model Advanced Behavioral Marketing Clusters and roles identification and behaviors prediction Thermodynamicalconditions and environment variable correlations Viral Marketing Optimization Media investment optimization by identifying the opinion makers/ebullition points… New Promotions/Plans/Bundles Acceptance Simulation Virus Quick Spread Prevention 5 Solutions for 5 main and totally different issues in a same company applying the same single Theoretical Model

63. Business Case: A Mobile OperatorMobile Network Recreation (NodeSnowball) FirstLevel Snowboard (Outcomingcalls ) FirstLevel Snowboard (SMS) Calldurationdistribution Color: Operatortheuserbelongsto Number of lines: Number of calls Thicknes: Averagecalldurationtothatnumber

64. Degreecentralitydistribution in a SFIN (Scale-Free Ideal Network) Someexamples of degreetypes Business Case: A Mobile OperatorNetwork Modeling. Nodesclassification Analysis of networkparameters (centrality, degreedistribution, clustering…) allowstoclassifynodes (both, company and competitorsnodesinteractingeachother) and identifythemostinfluential/vulnerable ones. Thatprovidescriticalinformationabouttheirbehavioralpattern (influentialpower, propagationcapacity, and theirrelative position versus theinterestgroups and the global position in thewholenetwork). Thatclassificationisregardlessothersociological variables (sex, ages, race…) thatwillonlybeusedwhendecidinghowtocommunicatethemessages in a Marketing campaign

65. Business Case: A Mobile OperatorMobile Network. ConnectionsStrength Evolution of 2 contacts’ connectionsweightsbased on a real mobileoperatorbill. Eachpeakrepresentsanevent. A highcallfrequencyhelpstokeephighthevalue of thecontactweightdespitethe natural decrease in time.

66. Business Case: A Mobile OperatorMobile Network. SuscribersConnections Illustration of a 1.000 nodesnetworkbased on the SFIN (Scale-Free Ideal Network). Itisclearly visible theGiantcomponent.

67. Business Case: A Mobile OperatorClustersidentification Themodulationprocessallowstoidentifycommoninterestgroups , associatedifferentnodesintoclusters, and hierarchically, clusteresintosuperclusters, based on local interactionpatterns, simplyfingnetworkanalysis at clusterslevelsinstead of nodeslevel.

68. Business Case: A Mobile OperatorCorrelationwithenvironment variables Thermodynamicconditionsidentification Thanksto Social ThermodynamicsTheory, weknownowthatthesizedistributions of thesegroupsdepends on a “Competitiveness” (λ) variable that defines thewayhumansformcommoninterestgroups (e.g.: if a givensocietygroups in manysmallclustersor in a fewbigones, etc) Thevalue of λ , representative of every social group, can beobtainedfrom a number of availablesources (publicdatabases, citiessizedistribution, electoral results, firmssizedistribution…) Thatnumber(λ) has a definitiveinfluence on thebehavior of theopiniontransmissionflows in a social network, as peopleconnections are thewaytheinformationgoesby. Red: Empirical Data Black: Social ThermodynamicsPrediction

71. Depending on local thermodynamicalconditions (temperature, pressure, density…) someareas are more sensitivetophasechangesthanothers.

73. It determines, in onesidethePhasechangeprobabilitydistributionwhereε1isanspecificsystemvalue and εo isanindicator of thecost/saving as a result of thetransition.

75. Thus, thealgorithmallowstofindout in thenetworktheweakestareas and thoseoneswiththehighestgrowthpotential, tooptimizeinvestment in churnprotection and new customersacquisition, protecting/increasingoperator’scustomer base

77. Thus, thealgorithmallowstofindout in thenetworktheweakestareas and thoseoneswiththehighestgrowthpotential, tooptimizeinvestment in churnprotection and new customersacquisition, protecting/increasingoperator’scustomer base

80. FollowingtheequivalencewithclassicalThermodynamics, ε0wouldrepresenttheaveragetemperature in a room, whileε1wouldrepresenttheeffect of turning on an oven or a refrigerator, i.e.the local manipulation of thetemperature.

81. “Social temperature” can bemodifiedand theresult of thatmodification can bePREDICTEDthanksto Social Thermodynamics

83. Empowerusersconsumptiontoincreaserevenues

85. Business Case: A Mobile OperatorVirus infectionanalysis Somestartingpoints can wronglybeconsidered as notdangerous, butthescenario can drasticallychangeifsensitivenodes are reached, and theprocessbecome a massiveattack. Generallythenumber of infectednodesgrowsgeometrically in earlystagestoslowdownwhen a saturationpercentage of infectednodesisreached. Fraction of networkinfectedin 10 differentsimulations Theprogress of theepidemy (contagionpotential) throughthenetworkwillhighlydepend on theinitialfocus(“source of fire”)

87. Classify nodes, i.e. dentify those users susceptible to accelerate the process if they get infected (most contagious) and, therefore…

88. Designprevention/defenseplans.Epidemyevolutionin time (t = 1, 3, 5 & 7) Red : InfectednodesGreen: Notinfectednodes

89. Numberof nodesinfectedin thenetwork(total nodes: 1.000) vs Time based on theprobaility of Infection (p) Fractionof Network finallyinfectedafterthe Virus spread, based on theprobability of infection (p) Business Case: A Mobile OperatorVirus infectionanalysis Toclassifynodesbyrisk of contagion, a largenumber of disseminationprocesses are simulatedvaryingtheprobability of infection. Depending on the time neededtoreachsaturation and the final fraction of networkinfected a value of infectionpowerisassigned, representingtheircapacitytogenerate and/oraccelerateepidemies.

90. Business Case: A Mobile OperatorVirus infectionanalysis Recreationof theinfectedcomponentand thesavedonesafterthe Virus spread for p = 0,3 Graphicalreproduction of thenetworkshowingthefirst 10 levels of infectednodes, fromtheinitialone.

91. ASThROAutomated Socio Thermodynamics Research Operative SThAR Marketing System, is a powerfulall-in-one marketing solutionthatprovidesto Marketing professionalsallthetoolstocreate, plan, launch, track and automatedirect marketing campaignsincluding and advanced Content Manager System (CMS). Based on theinformationobtainedfromthepreviousnetworkanalysis, modelling and predictivesimulations, itallowstoexecute , track and control different online marketing actions and generatethecorrespondingreportstoverifythesuccess of marketing campaigns , analyzedeviations and helptotakecorrectivedecisions .

92. ASThROFeatures (I) SubscribersTracker Youwillknowallyoursubscribers’ actionsthroughsent e-mail and web site Campaign Manager You can define and planify online marketing campaignsfortargettedemailing. Itincludesanapprovalworkflowtool, preliminar emails preview, sendscheduler… Suscribers Manager You can import, export and createautomaticallysuscriberslists, and add (OPT-in) oreliminate (OPT-out) customers at one-click SegmentedSuscribersList You can manageyoursuscribersbased on segmentationpredefinedcriteria, mergedifferentlistsintoone, managebyusersorbyusers’ profiles , etc.

93. ASThROFeatures (II) Events and Response Management You can control alltheeventsrelatedtoanydirect marketing campaign: sentmessages, readmessages, bounced/failedmessages… and analyze at oneclickthesuscriberhistory , and have a granular control. Real Time RedemptionAnalysis You can analyzetheresults of yourcampaigns in real-time, allowingtoverifytheeffectiveness of direct marketing promotions in launch time toquicklydetectdeviations and takecorrectiveactions. On-line Analysis of Off-Line Campaigns You can analysethesuccess of off-line marketing activitiesbygeography, by media, byadvertisingsupports (TV, press, radio…) throughtheinmediate off-line to on-line traslation. IntegrationwithClients/ExternalCRMs You can exporttheresults of theCampaignsAnalyzerto Excel and otherformatstobeused/integrated in externalCRMs.

94. ASThROFeatures (III) ClientProfilesGenerator Automatic + Manual indicators Advancedsuscriberssearch, byanyfield/list/indicator Possibilitytoadd new indicatorstopreexistingsuscribers/clusters Possibilityto define individual preferencesregardingdifferentconcepts: type of message, contactfrequency, language, email format… Business OportunitiesDesigner You can createyourownbusinessindicatorstogenerate new oppotunitiesalerts. Advanced Email Designer You can use thepredefinedtemplates and designsor use externalones. ItallowstomanageDocuments & Imagestostore in central servers allthecomponentsincluded in emails. AdvancedNewslettersDesigner You can design, applyormodifyexistingtemplatesforyourperiodical on-line marketing communications.

95. Business Case: A Mobile OperatorTheDefinitive MarketingWeapon

96. For more information: info@sthar.com What if reality was predictable ? Thank s foryourattentionquestions ? www.sthar.com