Pr Béatrice Galinon-Mélénec

Bourdieu, Goffman, Derrida, Eco, Ricoeur Bateson

Varela & Simondon

Seneca Spinoza Thomson

Reliance on digital traces vs deconstruction of their meaning

Keywords: Epistemology of the trace, -Definitions of the terms trace, mark, indication, fingerprint, Human-Trace, Sign-Trace, Body-Trace -Lettrure -Systemic approach -Complexity vs simplexity -Ethics

In this text, Béatrice Galinon-Mélénec analyses digital reality as a "black box of knotty implications for human beings" who were the very ones who created it. Her anthropological approach has led her to conclude that today's humans, like those of yesterday, are confronted individually with ethical choices: treating digital traces as Ichnos-Anthropos (Human-Trace) by studying the range of possibilities offered among Homo homini lupus or Homo sacra res homini.

Reliance on digital traces vs deconstruction of their meaning 1

Béatrice Galinon-Mélénec "We must begin somewhere where we are and the thought of the trace (…) already taught us that it was impossible to justify an absolute point of departure". (Derrida, 1967) 2 Introduction Scientific articles on the question of "digital traces" have multiplied over the last ten years. In the information and communications sciences, many are those who problematize the relationship between individual writing done on screen, acts of mediation that follow and powers associated with their implementation. Ethics aims to restrict the enormous capacity for traceability offered, and the effect on the individual that these practices bring about. According to their area of interest, researchers study the consequences of these behaviours in the area of private interpersonal relationships, the professional sphere, the public space, and analyse the legal rules likely to channel the negative effects. The result is a theoretical corpus of work rich in various conceptual approaches to which it would seem difficult to contribute any original ideas. However, we have taken the risk of approaching the complexity of the notion of trace (Jeanneret, 2011) from the perspective of the implications 3 and of proposing to explore the path of discovery to find the facets of the digital trace which still require clarification. This point of view is effectuated from the Human-Trace paradigm, a contemporary anthropological proposition devised in ICS (Galinon-Mélénec, 2011), the contents and associated notions of which should therefore be specified ("Body-trace", "Sign-trace 4 "). Development seeks to bring to light human responsibility in the choices that are made in the many invisible layers which develop digital traces culminating in a "screen reading". This aspect seems all the more important for us to examine in that the figure of a Human-Trace becoming a "wolf to man" stands out more and more frequently today, be it in the context of religious terrorism 5 , data hacking 6 , for financial or electoral purposes or to start up nauseating rumours 7 .

1 Translation by Isobel Hié (UK).

2 Derrida J., De la grammatologie, Paris, Minuit, 1967, p. 23. 3 In French : conséquence/trace 4 At this stage of the introduction, we shall say that, by referring to the sign as Sign-trace, we wish to highlight that observation transforms the Capta reality into a sign whose meaning appertains to implications/traces due not to the presumed author but to his interpretant. 5 Cf. "globalization and the digital revolution have contributed to the proliferation of the 'lone wolf' phenomenon", which have succeeded many times in carrying out actions, perpetrating several attacks in several countries", Libération of 9 April 2018: "What are the strategies for preventing terrorist groups from exploiting digital communication media?" https://www.libe.ma/Quelles-strategies-pour-empecher-l-exploitation-des-moyens-numeriques-de-communication-par-lesgroupes-terroristes_a96794.html 6 As we shall see, a "datum" results from a succession of sorting processes (figure 2). 7 Rumours of which Edgar Morin had already analysed the motives in the last century in Morin E., La rumeur d'Orléans, Le Seuil, 1 ère édition, 1969.

Other choices are possible: those that put digital traces at the service of knowledge and the common good.

As we shall see below, digital traces in themselves are "mute": they constitute a form of pharmakon 8 , offering not only new opportunities in the evolution of knowledge -as, for example, Computerized Decision Support Systems, which have introduced artificial intelligence (AI) into medical diagnosis 9 -new forms of risk prevention -such as, for example, the prevention of risks of epidemics 10 -but also dangers when it comes to privacy protection 11 or the likely emergence of threats which are to be feared all the more as they are as yet unknown.

The profiling of the individual is another double-faceted example. Mediations take place each time and the role played will understood better when Figure 1 has been read. Their operating traces reduce the complexity of the real and when the resulting Data become massive (Big Data) and are kept in cloud computing 12 the question arises of the implications of the meta power granted to the cloud manager over individual people's privacy.

On considering with Derrida that the possibility of trace is "a prior right to everything that is called sign 13 " and that "the order of the signified is never contemporary" 14 in that it is dependent on the trace, we introduced the notion of "sign-trace". Basing our approach on the notion of sign-trace, we arrived at the idea that, when humans attempt to go back to the first trace -the origin of which differs according to beliefs or scientific hypotheses -they reason using sign-traces which they locate historically.

In respect of sign-trace analysis, a starting point should therefore be set. One of writing (in the ordinary sense of the term) would seem all the more appropriate given that working on digital trace consists in part in questioning digital writing. As beginning this way makes it possible to see that contemporary issues on digital traces are not as new as they seem, we shall adopt it in 8 Pharmakon : In Greek, the pharmakon refers both to the remedy and the poison. Plato uses this term to explain that any remedy used to provide an improvement disrupts the system which in return produces non-neutral or even dangerous effects. The term pharmakon has been used by many philosophers after Plato. Among contemporaries, the most well-known is Bernard Stiegler who employs this term by contextualizing it to the analysis of the Internet after which, in the previous century, Jacques Derrida had himself recalled the ambivalent character of writing. Derrida J.,"Plato's Pharmacy" in: Dissemination, translated by Barbara Johnson, Chicago, University of Chicago Press, 1981,pp. 63-171. French version, Derrida J., « La pharmacie de Platon », dans La dissémination, 1972, p.77. http://www.occt.ox.ac.uk/sites/default/files/derrida_platos_pharmacy.pdf 9 AI (Artificial Intelligence) -Computer systems which exploit data by miming the cognitive procedures of human expertsis used in medicine as a decision aid. Computerized decision support systems continuously gather together publications, articles, works, abstracts, etc. This corpus, collected and interpreted, is used by doctors who are confronted with cases that are unfamiliar to them or rare in absolute terms. 10 It is apt to shield against risks of epidemics. But the experience of Google Flu trends shows some misinterpretations made in the capture of queries made by users on search engines: up to 20 August 2015 "when you typed in Google keywords like "flu", "symptoms", "thermometer", "muscle pains", "pulmonary congestion", "cough", the search engine did not list only the web pages in response to the internet user's request. These terms, typed by millions of web users worldwide, were analysed by an algorithm, country by country (29 exactly), to provide a localized and updated prediction of the peaks in flu even before the health authorities. (…) Although it was credible at the outset, the service in fact was shown to have over-estimated flu epidemics". In 2013, Google Flu Trends produced the American predictions which were 50% higher than those from the Centers For Disease Control and Prevention. And the predictions turned out to be false: Devillard A., "Google arrête de prévoir (mal) les épidémies de grippe", E-santé, 10.09.2015. 11 Risks to privacy are in principal established within a legal framework (for example, via the CNIL, Commission Nationale Informatique et Liberté -National Commission for Computing and Freedom); but, as we shall see below, risks remain. 12 Cloud computing: place where data are concentrated in huge amounts without users' knowing the location of the place storing their data. 13 Derrida J., De la grammatologie, p. 92. 14 Derrida J., De la grammatologie, p. 31. a central manner in this chapter and associate with it the very old notion of lettrure 15 .

Lettrure is a term that was already in use in France in the 12 th and 13 th centuries. It has been updated today because "the French language does not possess the equivalent of the English word literacy, which refers to the ability to read and write" 16 . We consider it also as a signtrace to signify -as we shall remind you throughout this chapter -that the sign without the presupposition of the trace is mute and that digital writing is a sign-trace that reading reifies.

(The rest of this article cannot be accessed on-line).

15 Lettrure: in the Middle Ages, referred to reading and writing activities perceived as a single activity. Cf. infra paragraph 4. 16 Maud Sissung cited in Souchier E., « La « lettrure » à l'écran. Lire & écrire au regard des médias informatisés », Communication & langages 2012/4 (N° 174), p. 85-108.


Thomas Maxime
email: maxime.thomas@mines-paristech.fr

Le Masson

Legrand Julien
email: legrand.julien@mines-paristech.fr

Benoît Weil
email: benoit.weil@mines-paristech.fr

Platform Overthrow: uncovering the critical role of functional extension and generic technology

Can a platform leader be challenged and loose its architectural control over its innovation ecosystem? The question seems absurd since theoretical works on modularity and multisided markets depict platform competitive landscapes as controlled by a hegemonic platform leader. However, platform history chronicles several cases of leadership shifts for the benefit of firms formerly providing complementary innovations. We coined these situations "platform overthrows". To bridge the gap between classical optimization models and empirical evidences of platform overthrow, we rely on a design-theory-based model (Legrand et al. 2017) of platform dynamics to generate testable hypothesis. We then test them with a sample of 22 empirical cases of attempts of platform overthrow. Our results indicate that platform overthrows are always built by a challenger that introduces a new functional range in the ecosystem. However, the efforts of the challenger can be ruined if the technology of the platform leader is easily adaptable to the new functional range. Otherwise, the challenger can overthrow its platform leader if it succeeds in designing a technology that can address both the former and the new functions. We conclude by highlighting how studying both technological and functional evolutions can provide a thorough understanding of platform ecosystem dynamics.

Introduction

Competition and its impact on innovation ecosystem evolutions have been receiving increasing attention, and [START_REF] Gomes | Unpacking the Innovation Ecosystem Construct: Evolution, Gaps and Trends[END_REF]) signals that the question of "how a firm leads an ecosystem and […] it's renewal when facing a competition based on a radical innovation" remains an open question. In this paper we extend current theory on innovation ecosystem competition by empirically testing a model of industry platform dynamic. (Annabelle [START_REF] Gawer | Industry Platforms and Ecosystem Innovation: Platforms and Innovation[END_REF]) define an industry platform as "products, services, or technologies developed by one or more firms, and which serve as foundations upon which a large number of firms can build further complementary innovations". Industry platforms are thus specific innovation ecosystems in which platform leaders -one or several firms-orchestrate innovation through a specific technical core [START_REF] Autio | Innovation Ecosystems: Implications for Innovation Management[END_REF] . Industry platform is a fruitful academic setting to study innovation ecosystem evolution as the current models on which the field is built fail to account empirical platform dynamics. Industry platform literature draws from two main concepts modularity and multisided-marketto explain how platform leaders build their successes. First companies such as Intel, Palm or DoCoMo rely on modular systems for their products [START_REF] Baldwin | Modularity in the Design of Complex Engineering Systems[END_REF]. Modularity enables other firmsthe complementorsto innovate by providing best-in-class modules, the platform leader being responsible for the overall structure of the product and the modules' compatibility. Second platforms leaders are able to successfully conduct multi-sided market strategies [START_REF] Rochet | Platform Competition in Two-Sided Markets[END_REF] towards their different groups of users. In a platform context, heterogeneous groups of users are subjected to indirect network effects: they mutually benefit from the size and characteristics of the other side. Therefore, platform leaders must harness the resulting network effects to ensure a high growing number of users and reach a critical mass that protect the platform leader from competition. Therefore, modularity and network effect models drive a trend in the literature that describes competitive platform landscapes as dominated by installed platform leaders for which small players can never be a threat. However, these theoretical approaches fail to provide consistent explanation for some empirical facts. Based on wellknown examples (see for example the early history between IBM and Intel), the industry platform literature recognized that a complementor can innovate in a platform-competing way and threaten the platform leader position (Anabelle [START_REF] Gawer | Bridging Differing Perspectives on Technological Platforms: Toward an Integrative Framework[END_REF]. In this paper, we therefore tackle this gap by quantitatively testing a model that account for shifts of leadership in industry platform ecosystem. The paper is therefore structured as follow. First, in the literature review, we indicate why the historic models are irrelevant to account for the dynamics occurring when there is an attempt of platform overthrow. We conclude that these models are static models that convey an underlying assumption of stability in the functional range that the platform performs. We then depict a model, previously published by the authors that encompass functional extension and provides relevant analysis of platform overthrow situations. We build from this model hypothesis that we test empirically on historic examples. Second, we described the methodology we follow to build a sample to test our hypotheses. Third, we present the results of our tests. Fourth in the discussion we highlight three striking facts that constitute our contributions to the field: 1) Platform overthrow is common phenomenon across the different platform sectors, 2) Functional extension is a necessary condition to undertake a platform overthrow attempt and 3) the success of such attempts is link with the ability to adapt the technology to address the newly introduced function, which is coined in the literature as technological genericity. We conclude with the next relevant avenue for research and our contribution to practice.

Literature review: The overthrow of a platform leader as an empirical fact revealing the underlying hypothesis of the classical platform models

Platforms and platform leaders have become major subjects in the academics fields for the past few years. In this section, we build on these constant academic progresses to highlight the issue of platform dynamic when submitted to competition in a Three-folded approach. First we point out that the literature reports on leadership shifts in platform ecosystems while the classical modelsmodularity and multi-sided marketare not accounting for them. We draw from the literature that the main weakness of these models relies on their underlying assumption of a stable functional range that the platform provides. Second we call upon the design theory literature to explain why model developed by the authors can encompass functional extension and provide insights on platform overthrow. Third, we deduce from the model relevant and quantitatively testable hypothesis.

Understanding platform overthrow as a way to bridge the gap between platform models and empirical evidence

Early academic work on platform industry was initiated in the end of the 90's to explain the success of several firms in software sectors that achieved fast market domination [START_REF] Steinberg | A Genesis of the Platform Concept: I-Mode and Platform Theory in Japan[END_REF]. Empirical evidence from the technological management literature that described how companies such as Intel, Palm or DoCoMo, coined as platform leaders, relied heavily on complementary innovations that ensure their market successes (Anabelle [START_REF] Gawer | Platform Leadership: How Intel, Microsoft, and Cisco Drive Industry Innovation[END_REF]. Two main bodies of theoretical works, from both strategic management and industrial organization economics literature have modeled underlying mechanisms that a platform leader uses to foster efficient complementary innovation. First, the modularity literature explains how platform leaders foster complementary innovations [START_REF] Baldwin | Modularity in the Design of Complex Engineering Systems[END_REF]. Thanks to modular systems some parts could be designed by "complementors" that are in charge of providing best-inclass modules, while the platform leader is responsible for the coherence of the overall structure and the modules' compatibility. Second the multisided-market models provided the theoretical prism to understand how to achieve fast market domination with complementary innovation [START_REF] Rochet | Platform Competition in Two-Sided Markets[END_REF]. The multisided market approach assumes the existence of at least two groups of heterogeneous platform users that are subjected to indirect network effects which means that different sides of a platform can mutually benefit from the size and characteristics of the other side. The more customers from one side join the platform the more the other side is interesting in joining, leading to a virtuous circle. Therefore a platform leader should develop complementary innovation so that ensure that both side join and stay on board. Thus by combining modularity and multi-sided network effects the academic literature provides good insights on the dynamics at stake for the rise of a successful platform leader. These straightforward models of platform dynamics are built on an underlying assumption that a platform leader, once established, remains in control of a thriving innovative ecosystem. However, these assumptions have been undermined by recent studies on platform ecosystem evolutions. Thus, based on empirical observations, several scholars point out that industry platforms are not stable structure overtime and different stages of industry platform (Anabelle Gawer 2014; [START_REF] Thomas | Architectural Leverage: Putting Platforms in Context[END_REF]. They describe that a platform ecosystem can be damaged to a point where the platform leader can no longer orchestrate innovation. As if this prospect was not worrisome enough for platform leaders, the literature accumulates examples where platform leaders lose their architectural control for the profit of a complementor -see for example IBM losing its architectural control on the PC platform for the profit of Intel (Anabelle [START_REF] Gawer | Platform Leadership: How Intel, Microsoft, and Cisco Drive Industry Innovation[END_REF], Nokia facing the rise of Microsoft OS and losing control over the Symbian platform [START_REF] Kenney | Structuring the Smartphone Industry: Is the Mobile Internet OS Platform the Key?[END_REF] and Yahoo! becoming Google's complementor [START_REF] Rindova | Partnering Portfolios, Value-Creation Logics, and Growth Trajectories: A Comparison of Yahoo and Google (1995 to 2007)[END_REF]. We coin in this article these situations as platform overthrow. Nurtured by these empirical examples, the literature gradually identified that the existing models of platform dynamics were unable to account for platform overthrow and that this subject constitute a relevant gap for research. In the critics concerning these models that arise in the literature, two trends can be identified: one on functional extension and the second on technology evolution. First, the existing models cannot account for feedbacks induced by functional extension which is the result of the leader's efforts to foster innovation. Theses authors highlight that a poorly managed functional extension can weaken a platform leader. If the leader misses some important functions, it may face radicalinnovation-based competition [START_REF] Gomes | Unpacking the Innovation Ecosystem Construct: Evolution, Gaps and Trends[END_REF]) and be submitted to platform leaders dilemma [START_REF] Cusumano | The Platform Leader's Dilemma[END_REF][START_REF] Gawer | Industry Platforms and Ecosystem Innovation: Platforms and Innovation[END_REF]. Second, although the existing models look for neglect technological changes that a platform leader conduct on its technical core in order to remain the innovation orchestrator which have been recognized to understand important aspects of platform dynamics such as organization (Anabelle Gawer 2015), architectural control of the ecosystem [START_REF] Autio | Innovation Ecosystems: Implications for Innovation Management[END_REF]) and regulation of ecosystem (K. [START_REF] Boudreau | Platforms Rules: Multi-Sided Platforms as Regulators[END_REF]. These two folded criticson functional extension and core technical evolutionhas been noted in recent literature review and (McIntyre and Srinivasan 2017) note that "There is relatively little understanding of platform dynamics and their evolution, with platforms being treated as systems that remain relatively stable overtime […] As a result, additional research is needed that focuses on how a platform firms manage and leverage their portfolio of complements during regimes of frequent technological change". Therefore understanding how a complementor can overthrow a platform leader could lead to bridge the remaining gap in the description of platform dynamics. As our literature review reveals, understanding platform overthrow is both a legitimate and interesting question for research. In particular, it can allow the field to bridge the gap between model based on an underlying hypothesis of a stable set of functions while empirical evidence suggest that functional extension play a role in platform completion as well as the capability of the platform leader to adapt its technical core. Therefore we look in the literature for models that can help us answer the following research question: what are the relevant strategies for a complementor to take the architectural control of an innovation ecosystem at the expense of its former platform leader?

Paper submitted to: R&D Management Conference 2018 "R&Designing Innovation: Transformational Challenges for Organizations and Society" June, 30 th -July, 4 th , 2018, Milan, Italy 4

The design theory literature as a promising academic setting to provide a valuable model of platform overthrow

To better understand platform dynamics, we previously saw that new models were needed with the following features. First, these new models should be able to take into account new functions and the capability of the technological core to evolve. In line with the work on modularity [START_REF] Baldwin | Modularity in the Design of Complex Engineering Systems[END_REF] which drew from engineering design literature to build relevant platform models, we claim that design theories [START_REF] Hatchuel | A New Approach of Innovative Design : An Introduction to C-K Theory[END_REF][START_REF] Shai | Infused Design. I. Theory[END_REF] are good settings to develop model that could be able to account for platform overthrows. The axiomatic design theory [START_REF] Suh | Development of the Science Base for the Manufacturing Field through the Axiomatic Approach[END_REF] seems particularly appropriate as it studies the links between functions and technological evolution. The authors, in a precedent study, developed a model for platform overthrow using axiomatic design theory [START_REF] Legrand | Rise and Fall of Platforms: Systematic Analysis of Platform Dynamics Thanks to Axiomatic Design[END_REF]. They also proved the consistency of their model with former platform models that used Design Structure Matrix [START_REF] Baldwin | The Architecture of Platforms: A Unified View[END_REF][START_REF] Eppinger | Model-Based Approaches to Managing Concurrent Engineering[END_REF]. The aim of this paper is not to linger on the model but to build from it testable hypothesis in platform context which is what constitute the next section.

Hypothesis formulation

The work conducted on platform overthrow is motivated by the observation that the classical models describing platform dynamics are built on the underlying assumption that the main functions provided by the platform remain stable overtime. More precisely, modularity models and multi-sided market models assume that a platform leader can improve existing functionsor have them improved by complementorsbut cannot foster functions that address new value proposition for the customer. On the contrary, our model indicates that functional extension is a necessary condition to conduct a platform overthrow. Therefore, to confirm the close links between platform ecosystem dynamics and functional extension we formulate our first working hypothesis: H 1 1: Every attempt of platform overthrow is built on a functional extension originating from the challenger. This first hypothesis gives a necessary condition to undertake an attempt of platform overthrow. Nevertheless, the model provides also insights on the outcome of such attempts. First the model clarified what can be called a success for the leader and for the challenger in context of platform overthrow. From an incumbent platform leader point of view, success is achieved when it can remain in architectural control of the ecosystem therefore when the challenger has to leave the market or become a complementor of the incumbent platform leader. From a challenger point of view, success is achieved if it takes control of the totality or some parts of the architectural control on the platform. The model therefore encompasses the asymmetry between incumbent and challenger. The model predicts that the success of one player is linked with its capacity to propose a generic technology. The genericity of a technology is a notion that comes from the general purpose technology literature [START_REF] Bresnahan | General Purpose Technologies "Engines of Growth[END_REF], qualifying the ability of a technology to be adapted to address new functions. A technology is generic if it can, while minimizing the design effort, address an enhanced functional range [START_REF] Kokshagina | Platform Emergence in Double Unknown: Common Challenge Strategy[END_REF]. The hypothesis we formulate from the model is the following: H 2 1: To achieve success in case of a platform overthrow, one must control a generic technology.

This second hypothesis calls for two remarks. First, the hypothesis still takes into account the asymmetric situation between the two competitors. The challenger technology is generic if it can address the new functions and at least some of the former functions (but not necessarily all of them). The platform leader technology is generic if it can address the functional extension provided by the challenger. In that case, the model predicts that the challenger is unable to propose a generic technology since it would mean to build the entire platform already possesses by the incumbent. Second, this hypothesis is in line with the work on envelopment strategies [START_REF] Eisenmann | Platform Envelopment[END_REF]. These authors describe a possible strategy for a platform leader to counter an attack from a new entrant. To do so, the platform leader should include for free in its current offer the competitor functions. This strategic move decreases the switching costs for the consumers in particular when the entrant and incumbent user bases overlap. Thus the incumbent can capture the consumers of its competitors and therefore avoid platform overthrow. Our hypothesis are aligned with this contribution since the envelopment strategy assumes that the competitors provide a functional extension (Hypothesis 1) that has to be included in the platform leader offer through its existing technology (Hypothesis 2).

Paper submitted to: R&D Management Conference 2018 "R&Designing Innovation: Transformational Challenges for Organizations and Society" June, 30 th -July, 4 th , 2018, Milan, Italy

5

These hypothesises could lead us to describe the underlying mechanisms occurring in platform overthrow context. If H2 is confirmed, we could formulate relevant strategies for both the incumbent platform leader and the platform challenger. However we should compare our explanatory variablesfunctional extension and genericity -with the straightforward explanations of platform weakening in the literature. Thus several scholar already studied platform weakening mechanisms such as a competition with an already established platform [START_REF] Cennamo | Platform Competition: Strategic Trade-Offs in Platform Markets: Platform Competition[END_REF], a better pricing strategy provided by the new entrant to ensure a quick critical mass (Evans and Schmalensee 2010) and a takeover by the challenger [START_REF] Eisenmann | Platform Envelopment[END_REF]. Therefore we formulate a final hypothesis: H 3 1 : The explanatory variables of platform leader competition, pricing strategy and takeover better explain the outcome of platform overthrow than the two explanatory variable functional extension and genericity

3) Experimental method: Quantitatively test the hypothesis on a highly qualitative sample

To evaluate our hypothesis concerning platform strategy, we aim at using a quantitative methodology. However, we are aware that it might be too bold of a choice since the complexity of strategic actions taken by platform leaders lead scholars to focus on cases studies (de Reuver, Sørensen, and Basole 2018). Quantitative methods have been used to study platform ecosystems (K. J. [START_REF] Boudreau | Let a Thousand Flowers Bloom? An Early Look at Large Numbers of Software App Developers and Patterns of Innovation[END_REF][START_REF] Ceccagnoli | Cocreation of Value in a Platform Ecosystem: The Case of Enterprise Software[END_REF], nevertheless they always focus on one particular platform ecosystem. Therefore there is, to the best of our knowledge, no quantitative study assessing the relevance of a given strategy across several platforms sectors because of the difficulty to provide a general framework that makes sense for all platform ecosystems. We hope that platform overthrow constitute a framework that enables us to conduct a relevant analysis across platform sectors. However, due to the difficulty to gather a high number of cases, we strengthen the humble tests we conduct with a highly qualitative sample and analysis of cases. In the following of the section, we present the sample collection method and the analysis conducted. The main idea of this experimentation is to review previous cases related to platform overthrow and to code them with the functional extension and genericity variables.

Gathering an heterogeneous and qualitative sample of platform overthrow attempts

To quantitatively test our hypothesis, we build a sample composed of situations in which a challenger attempts to overthrow a platform. To do so, we analyze long term history of well-known platforms thanks to academic description of those cases. Relying on academic works studying long term platform evolution as the primary source for our data ensures the quality of our sample in three interrelated ways that are consistent with our research question. First, it enables us to include in the sample cases in which the challenger failed to overthrow the platform. We therefore avoid the bias of focusing only on emblematic cases where platform overthrow occurred. Second, the requirements of academic work allow for fruitful cases descriptions. This point is crucial in our work since we look for variablesfunctional extension and genericity extensionthat require to report on both technical and value proposition aspects. Third, by analysing sources explicitly related to platform body of literature, we validate that the cases included in our sample are platform related and not belonging to different competition logic. In coherence with our methodology setting, our primary sources consist in two main books related to platform long term history (Anabelle [START_REF] Gawer | Platform Leadership: How Intel, Microsoft, and Cisco Drive Industry Innovation[END_REF][START_REF] Evans | Invisible Engines: How Software Platforms Drive Innovation and Transform Industries[END_REF] and several academic papers (Den [START_REF] Hartigh | Platform Control during Battles for Market Dominance: The Case of Apple versus IBM in the Early Personal Computer Industry[END_REF][START_REF] West | Evolving an Open Ecosystem: The Rise and Fall of the Symbian Platform[END_REF][START_REF] Ansari | The Disruptor's Dilemma: TiVo and the U.S. Television Ecosystem: The Disruptor's Dilemma[END_REF] that specifically aim at describing long term history of a platform ecosystem. To spot in those documents cases of attempt of platform overthrow we look for unambiguous statement from either the challenger or the platform leader that a specific strategic move falls within an attempt of platform overthrow. Applying this methodology we build a sample of 22 cases of platform overthrow attempts. Table 1 elucidates for each source the number of cases found and one example of a case with the names of the platform, and the citation that make them belong to the sample.

----------Insert Table 1 here ----------Paper submitted to: R&D Management Conference 2018 "R&Designing Innovation: Transformational Challenges for Organizations and Society" June, 30 th -July, 4 th , 2018, Milan, Italy 6

Coding the cases to allow quantitative analysis

To undertake the analysis of our sample according to the hypothesis we build, we undertake a textual coding of our case descriptions. In coherence with our model, we extracted quotation in case description that related to the variable at stake. For the functional extension variablelabelled Frwe look for quotations that compare the incumbent platform and the challenger functions. For the incumbent genericity variable -labelled G inc -we looked for quotations that indicate that the platform leader was able to adapt its technology to the new functions easily. For the challenger genericity variable -labelled G cha -we look for quotations that indicate the challenger was able to provide a technology that encompasses the previous function of the platform leader. For the success variablelabelled S cha for the success of the challenger -we look for quotation that could indicate whose firm succeeded. Then according to these citations we attribute for each variable the Boolean value 0 or 1 accordingly. For illustrative purpose, the table below details examples of our analysis.

----------Insert Table 2 here ----------

4) Results

Quantitative results (Hypothesis 1 and Hypothesis 2)

As described above, we characterized each cases of attempt of platform overthrow with three variables: success of the challenger S cha (which is the complementary variable of the platform leader success S inc ), functional extension provided by the challenger Fr and technical genericity coming from either the challenger G cha or the incumbent G inc (these two variables being also complementary).

H 1 1 hypothesizes that the challenger undertakes a platform overthrow by providing a functional extension. We test the hypothesis H 1 0 that the variable functional extension follows a binomial law with a one-sided 1% trust interval. In our sample, 22 cases of attempt out of 24 were built with a functional extension while the probability for Fr following a binomial law with p=0.5 to be above 22 is 0,999. Thus we reject H 1 0 and formulate a dependency between the variable Fr and the belonging to our sample. We therefore conclude the following for H 1 : An attempt of platform overthrow cannot be undertaken by a challenger without proposing a functional extension compared to the previous platform.

H 2 1 hypothesizes that the winner of a situation of platform overthrowmay it be the challenger or the platform leaderis the one that extend the genericity of its technology. We test the hypothesis H 2 0 which hypothesizes a uniform distribution among the four different sectors: Success of incumbent with genericity (S inc = 1 and G cha = 0), success of incumbent without genericity (S inc = 1 and G cha = 1), success of challenger with genericity (S inc = 0 and G cha = 1) and success of the challenger without genericity (S inc = 0 and G cha = 0). We test H 2 0 with a χ 2 test for a two-sided 0,5% confidence interval. We obtain a total distance of 20.0 which is superior to the table distance of 7.8. Thus we rejected H 2 0 and formulate a non-uniform distribution of the success in a platform overthrow context and the genericity of the winner's technology. Therefore, with respect to H 2 , we conclude as follow: In a situation of platform overthrow the success goes to the player whose technology is generic. We insist that genericity for the leader and for an incumbent is different but so does is the success as a challenger will be consider a success if it just remains on the market. Now that we have shown a relationship between to model-driven variables and the success of a platform overthrow strategy, we analyse these phenomena in more depth. In particular, we underline that those two variabletechnological genericity and functional extensionexplain better the outcome of an attempt of platform overthrow than the classical descriptors of platform competition namely multi-sided market pricing strategy and take over. To correctly test H3, we therefore should have used a logistic regression; however the number of cases is not enough to provide testable results. Therefore we rely on an in-depth analysis of two extreme cases concerning pricing and take-over. For the pricing analysis, we rely on cases brought to us by the book of [START_REF] Evans | Invisible Engines: How Software Platforms Drive Innovation and Transform Industries[END_REF] which belong to the economic academic field. Several clues in their work tend to prove that establishing a pricing strategy is not used at all to undertake platform overthrow. Quantitatively, only in only one out of seven cases in our sample do the authors indicate that a new pricing structure is used by a successful challenger which rises their attention as the authors "It is natural to ask how software platforms' pricing strategies evolve over time. Based on our case studies, the surprising answer is, to a first approximation, they don't. Both what is priced and the basic pricing structures tend to remain constant over time. The only major shift in pricing strategy that we have observed occurred in 1977, when Atari began selling its new VCS game console below manufacturing costs". Therefore, competition for platform leadership seems not to trigger several pricing structures in the way the sides of one platform are addressed. This is a "surprising" fact for the authors as they also note that the pricing structure differs between several platform industries and therefore one new comer could be tempted to disrupt the industry with a new pricing structure.

For the take-over variable, our argument rely both on empirical and theoretical observation. When analysing the sample we collected, we realize that Cisco is the company that is known for regularly take over potential rivals. However, this approach of Cisco is described as being completed with a hard work to adapt their technology to the newly acquired company. Cisco regularly change its core, we would say extend its technologic genericity, while taking over other companies. Therefore, we think that the variable "take over" is encompassed by the "genericity extension" variable.

Our second argument is theoretical and grounded in the model provided by [START_REF] Legrand | Rise and Fall of Platforms: Systematic Analysis of Platform Dynamics Thanks to Axiomatic Design[END_REF]) and our hypothesis H 1 and H 2 : if a company had acquired a platform challenger, this challenger had proposed a functional extension that is threatening for the platform leader (Hypothesis 1). Once acquired, if the platform fails to integrate the functional extension in its technology (Hypothesis 2), what prevents a new challenger to rise? Therefore both theoretical argument and empirical argument indicates that the pricing strategies and takes over are not the best explanatory variables to account for platform overthrow situations.

5) Discussion

Main contribution

Our main contribution to the platform literature is twofold. First, we legitimate the notion of platform overthrow through establishing a consistent sampling a those cases. The platform overthrow analytical setting enables us to conduct the first multi-platforms quantitative study. Second we strengthen the notion of platform overthrow by testing general laws accounting for the dynamics at stake in such cases. We first demonstrate that a functional extension is a necessary condition to undertake a leadership change in a platform ecosystem. Second we highlight the role of technical evolution in the outcome of a platform overthrow situation. In particular, we established that to foster a successful outcome both challenger and established platform leader should be able to adapt their technology so that it addresses the newly introduced function. This capacity being called in the literature genericity, we summarize our contribution by stating that to succeed in the case of platform overthrow onemay it be the platform leader of the challengermust propose a generic technology. More precisely, we show that if the platform leader technology is generic it maintain its leadership and if it is not, then the more the challenger technology is generic, the more architectural control it will gain at the expanse of the platform leader.

The high level of analysis that we have been able to conduct in this study is largely due to previous works from the platform literature and remain consistent with them. First, our results confirm and generalize the exemplary work on platform envelopment strategy [START_REF] Eisenmann | Platform Envelopment[END_REF]. Platform envelopment can be analyzed as a particular strategy a platform leader can implement to counter an attempt of platform overthrow. Second the literature has for long suspected the existence of specific strategies [START_REF] Suarez | Dethroning an Established Platform[END_REF] aiming at weakening the platform leader position (Anabelle Gawer 2014). Our work provides a setting that can help conducting systematic analysis of those situations. Finally, the literature has for long acknowledge that platform leaders implement sophisticated strategies to strengthen their architectural control on their ecosystem [START_REF] Gawer | Platform Owner Entry and Innovation in Complementary Markets: Evidence from Intel[END_REF][START_REF] Perrons | The Open Kimono: How Intel Balances Trust and Power to Maintain Platform Leadership[END_REF]

Limitations

As highlighted throughout this paper, our research has strong limitation. The first and obvious one is linked with the small number of cases we have been able to gather so far because of our demanding sampling methodology. This limitation weakens the quantitative analysis that we conducted. While this limitation may have seriously misleading effects on the second hypothesis from a statistical point of view, basing our hypothesis on a model strengthens our analysis and seems to indicate global trends that support our findings. We are currently working to enhance our sample with other highly qualitative sources describing platform history. A second limitation is linked with the third hypothesis which needs logistic regressions to be properly tested. Once again, we hope that the enhancement of our sample will eventually enable us to conduct such analysis. A third limitation is grounded in the methodological approach. As we used secondary data to build our sample, we could be submitted to misinterpretations of platform histories. We hope that our approach can be confronted to several practitioners or platform specialists so as to triangulate our data and strengthen our analysis.

Further research

In line with the literature, we call for more systematic analysis of platform dynamics and claim that merging marketoriented and technological approaches opens avenues for further researches. More precisely, we encourage scholars to investigate the functional dynamics of a platform ecosystem and their underlying technical evolutions instead of concentrating on pricing strategies. These works has been undertaken at a firm level, we call for studies that conduct such analyses at an ecosystem level. These works could aim at developing new types of regulations based on dynamics model of competitive landscapes. For examples, scholars could investigate under which conditions new functions emerge and how it impacts value capture in a given ecosystem. Furthermore, managing functional extensions at an ecosystem level requires organization, skills and resources that still have to be characterized. We also encourage scholars, in particular ones working with the innovation ecosystem construct to cross fertilize with model oriented body of research such as design theories in order to build contributions for further research. Therefore, we hope our research will contribute to foster fruitful exchanges between innovation ecosystem and the technology management literatures such as those initiated by [START_REF] Kokshagina | Platform Emergence in Double Unknown: Common Challenge Strategy[END_REF] with the notion of generic technology.

6) Conclusions and managerial implication

The results of our research include several new findings and confirm certain results in the literature. The platform overthrow prism is relevant to analyse platform dynamics and compare them across industrial sectors. Functional extension and the evolution of the technical core are two keys features to understand quick leadership shifts in platform ecosystems. These two variables account for the cases related in the literature.

The platform overthrow framework enables manager to conduct new analysis on their competitive ecosystem. First, our research provides simple criteria to guide practitioners in their competitive benchmarks across platform ecosystem. A complementor could be a real threat only if it performs new functions with a generic technology. Second our work indicates two crucial ways to enrich a platform strategy: thriving for functional extension and technological genericity. Therefore we hope that these features will help managers to assess the performance of their current technology and indicate ways to improve them.

Table 1 .

 1 Paper submitted to: R&D Management Conference 2018 "R&Designing Innovation: Transformational Challenges for Organizations and Society" June, 30 th -July, 4 th , 2018, Milan, Italy 7 Sources contribution description

	Source reference	Number of cases from the source

Table 2 .

 2 Two examples of case analysis

	Incumbent vs. Challenger	Fr	Quotation Fr	G inc =	Quotation G inc =	S cha	Quotation S cha
					1) "This was coincident with IBM proving either lack of
					interest or inaptitude in moving the PC platform forward"
					(p.26)
					2) "The development of these chip sets [of IBM] or computer
	IBM vs. Intel		1) "We picked [Intel's PCI] because it solved a range of problems: It solved a graphics		platform was getting much more complex. So, it was getting increasingly difficult for the larger OEMs to keep up" (p29)		1) "The initiative was a resounding success"
	(Anabelle Gawer and Cusumano 2002)	1	problem, it solved plug and play problems, it solved interrupt problems, it solved performance issues"	0	attached to the bus" (p.29) 3) "The PCI bus [of Intel] was an internal interface whose definition potentially affected all the companies that design and manufactured other parts of the computer	1	2) "Thanks to PCI, Intel grabbed the position of platform leader"
					4) "You've got 500 vendors out there in the industry that are
					providing add-in board for various functions that plug
					into PCI" (p.31)
					1) "A major disadvantage of the original Palm hardware
	Palm vs. Handspring (Evans, Hagiu, and Schmalensee 2006)	1	1) "This expansion slot allowed owners of the Handspring Visor Deluxe to attach other hardware modules to make their PDA a pager, mobile phone or voice recorder" (p.166) 2) "The colour unit […] was another feature of the […] Handspring models" (p.167)	0	architecture (especially in contrast to SpringBoard) was the lack of an expansion slot or standard connector that made it easy for third parties to add on modules." (p.208) 2) "Unlike Palm, […], Handspring engineers designed the hardware […] in a bold move to make modules or peripherals as easy as possible to connect. The expansion modules literally snapped into the expansion slot on the back of the Visor PDA. Palm device lacked such a simple mechanism for expansion when Handspring introduced this innovation." (p.206)	1	1) lead in defining price points for PDAs " Handspring had taken the as well as hardware design" (p.208) 2) "Although Handspring was helping to build broader acceptance of Palm OS as a standard platform, the company seemed to be doing more than Palm to drive forward that PDA architecture and functionality"

  . OurPaper submitted to: R&D Management Conference 2018 "R&Designing Innovation: Transformational Challenges for Organizations and Society" June, 30 th -July, 4 th , 2018, Milan, Italy work underlines the importance for the platform leader to anticipate functional extensions that could occur in its ecosystem. The results from our research, that highlights functional extension and genericity as key mechanisms, structure notions that are already present in the literature. Functional extension has for long be scrutinized by the field, in particular to find the dimensions that can stimulate it such as closeness versus openness (K. Boudreau 2010), pricing structures[START_REF] Hagiu | Two-Sided Platforms: Product Variety and Pricing Structures[END_REF] or the number of complementors (K. J.[START_REF] Boudreau | Let a Thousand Flowers Bloom? An Early Look at Large Numbers of Software App Developers and Patterns of Innovation[END_REF]. Technical evolution and genericity have often be referred tothough with other termsin recent researches that highlight their importance to understand platform dynamics[START_REF] Mcintyre | Networks, Platforms, and Strategy: Emerging Views and next Steps: Networks, Platforms, and Strategy[END_REF] Anabelle Gawer 2015).

Paper submitted to: R&D Management Conference 2018 "R&Designing Innovation: Transformational Challenges for Organizations and Society" June, 30 th -July, 4 th , 2018, Milan, Italy Paper submitted to: R&D Management Conference 2018 "R&Designing Innovation: Transformational Challenges for Organizations and Society" June, 30 th -July, 4 th , 2018, Milan, Italy


Kei-Léo Brousmiche
email: kei-leo.brousmiche@lip6.fr

Jean-Daniel Kant
email: jean-daniel.kand@lip6.fr

Nicolas Sabouret
email: nicolas.sabouret@limsi.fr

François Prenot-Guinard
email: francois.prenot-guinard@airbus.com

Stéphane Fournier

From Field Data to Attitude Formation

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The new conflicts that arouse during the two last decades have brought a deep shift in military strategies [START_REF] Smith | The utility of force: the art of war in the modern world[END_REF]: most of the stabilization operations conducted by western forces involves opponents who blend themselves into the "human environment" to turn the population in their favor. In order to counter them, one should not only rely on tactical actions against them but also on non-kinetic actions such as reconstruction or specific communication actions that aim at altering the "hearts and minds" of the population. In this context, understanding the impact of actions performed by the intervention Force on the population's attitude is a major issue.

The concept of attitude derives from social psychology and could be defined as "a mental and neural state of readiness organized through experience" [START_REF] Allport | [END_REF]. Multi agent simulation of attitude dynamics seems a promising approach to study such complex social phenomenon since it is funded on individuals micro modeling and their interactions to analyze emergent macro trends [START_REF] Drogoul | Multi-agent simulation as a tool for modeling societies: Application to social differentiation in ant colonies[END_REF]. While multiple agentbased models have been proposed to study attitude and opinion (i.e. expressed attitude) dynamics [START_REF] Deffuant | Mixing beliefs among interacting agents[END_REF][START_REF] Urbig | Drifting to more extreme but balanced attitudes: Multidimensional attitudes and selective exposure[END_REF][START_REF] Brousmiche | The role of emotions on communication and attitude dynamics: an agent-based approach[END_REF], the major difficulty relies in validation: can a given model correctly reflect the attitude dynamics of a population in a conflict zone? Opinion polls can provide target values. However, collecting field data to feed the simulation model and assessing the validity of its outcome (based on the expected values) can prove to be very difficult.

As part of our research, we have been given an access to polls results about opinions of the population toward the different present Forces (foreign Force and Taliban) in an area of Afghanistan where French forces conducted stabilization operations 4 . Along with these survey results, we have reconstituted the military actions sequences of each Force through a series of interviews with officers who were in charge of the situation. Our analysis of these data has brought light to attitude dynamics that go against classical approaches in social simulation. First, most models compute the attitude as the aggregation of the impact of each feature, seen as independent criteria or events [START_REF] Urbig | Drifting to more extreme but balanced attitudes: Multidimensional attitudes and selective exposure[END_REF][START_REF] Deffuant | Mixing beliefs among interacting agents[END_REF]. However, people do not evaluate each action (such as food provision, military patrol, bombing attack, etc.) independently but towards what similar actions represents in general in terms of direct and indirect consequences for the population. It corresponds to Kahneman's memory-based, retrospective approach of evaluation [START_REF] Daniel Kahneman | Experienced utility and objective happiness: A moment-based approach[END_REF]. Second, we noticed that population's attitude toward the UN army could decrease when it fails to accomplish its securing mission. For instance, in case of a bombing attack, victims will not only blame insurgents who are directly responsible but also the Security Force which have "failed" to prevent such an event. It corresponds in this case to the concept of "role-responsibility" in the sense of Hart [START_REF] Lionel | Punishment and responsibility: Essays in the philosophy of law[END_REF]. More generally, people tend to attribute the responsibility of an action to other people, groups or situational factors in addition to the direct responsible [START_REF] Kelley | The processes of causal attribution[END_REF][START_REF] Jones | The attribution of attitudes[END_REF].

In this paper, we propose a multi-agent simulation model based on field data that will help to better comprehend attitude dynamics in conflict zones where the population is confronted to antagonists Forces.

Related works

Several researches have already proposed computational models of attitudes, from simple binary approaches [START_REF] Nowak | From private attitude to public opinion: A dynamic theory of social impact[END_REF] to more complex ones (e.g. [START_REF] Kottonau | Simulating political attitudes and voting behavior[END_REF]). However, as was pointed out by [START_REF] Castellano | Statistical physics of social dynamics[END_REF], most of these models' focuses are limited to the interactions between individuals: they do not consider the construction mechanism of the attitude itself at a cognitive level. On this matter, Urbig and Malitz [START_REF] Urbig | Drifting to more extreme but balanced attitudes: Multidimensional attitudes and selective exposure[END_REF] proposed to represent attitudes as the sum of the evaluations of the object's features that can be seen as beliefs on the object, so as to take into account the cognitive aspect. While this model constitutes an interesting view on attitude formation, it has two limits with respect to our objectives.

First, the attitudes' values are not connected to the beliefs of each agent constituting their personal history. Indeed, their attitude revision mechanism is based on the bounded confidence model (e.g. [START_REF] Deffuant | Mixing beliefs among interacting agents[END_REF]): when two individuals have attitude values close to each other, agents converge their attitudes. However, it could be possible to combine this with Fazio's model of attitude [START_REF] Russell | Attitudes as object-evaluation associations of varying strength[END_REF]. This model connects the attitude to its forming beliefs as a set of memory associations.

Each of these evaluations is weighted by an accessibility value determining the evaluation's degree of reminiscence. By essence, this model maintains a balance between the cognitive representation of the object and its corresponding attitude.

Second, the attitude model of Urbig and Malitz does not embody an emotional component, while social psychologists have defined the attitude as embodying rational and affective components [START_REF] Rosenberg | Cognitive, affective, and behavioral components of attitudes. Attitude organization and change: An analysis of consistency among attitude components[END_REF][START_REF] Russell | Attitudes as object-evaluation associations of varying strength[END_REF]. This is the reason why Brousmiche and al. [START_REF] Brousmiche | The role of emotions on communication and attitude dynamics: an agent-based approach[END_REF] attempted to combine Fazio's model with the Simplicity Theory proposed by Dessalles [START_REF] Dimulescu | Understanding narrative interest: Some evidence on the role of unexpectedness[END_REF] which embodies an affective response mechanism. However, their model does not consider (1) the aggregation of beliefs into a personal history as proposed by Fazio and (2) the notion of role responsibility impacting attitudes toward other actors than perpetrators of an action.

In this paper, we will take the model presented in [START_REF] Brousmiche | The role of emotions on communication and attitude dynamics: an agent-based approach[END_REF] as a starting point and extend it in order to take into account these two concepts by adding a mechanism of co-responsibility and modifying the aggregation method of the attitude computation.

Field data

Scenario In the course of the NATO intervention in Afghanistan to stabilize the country, the French Forces were tasked to maintain security in the regions of Kapisa and Surobi between 2008 and 2012. It is in this context that members of CIAE 5 were sent in the area from October 2010 to September 2012. Through a set of six interviews with all the successive officers in charge (3 colonels and 3 commanders) from the Joint Command for Human Terrain Actions, we managed to rebuild the sequence of the events that took place during their tenures, originating both from the NATO and from the Taliban insurgents. This sequence takes the shape of a scenario (see figure 1).

Each action is characterized by a reach, a frequency and a payoff: how many people were directly affected by the action, how many times per week if it is frequent, how each individual is impacted. These values were defined based on subjective assessments of domain's subject matter experts (including members of CIAE). For instance, we defined the action "patrol" as being triggered by the Blue Force, affecting the population with a positive impact of 30 and a reach of 20 people, repeated 3 times per week in average. Similarly, "IED" (Improvised Explosive Devices) are done by Red Forces and affect one individual with a payoff of -100. The number of victims of each attack were defined according to open source records (e.g. [START_REF] Ema | Ensemble vers l'autonomie[END_REF]).

We can observe that both Forces have a constant background activity toward the population composed of non-kinetic actions. However, the Red Force activity is heavily decreased during winter which corresponds to the second period on the scenario. One reason is that local Taliban leaders leave the region to avoid the arid climate. Also, the surveillance systems, including drones, are more effective in the absence of foliage, making it more difficult for insurgents to place discretely the IEDs. On the Blue Force side, the activity decreases constantly due to the political decision taken after the big human losses on the first period. Indeed, the French government began to adopt a "retreat" strategy after the suicide attack of Joybar (July 13th 2011) which caused considerable human casualties among the Blue Forces.

Opinion polls In order to follow the progress of population's sentiments and to link them to foreign Forces activities, the French Ministry of Defense financed opinion polls in Afghan regions where the French forces were operating. Those surveys were conducted by Afghan contractors between February 2011 and September 2012 with an interval of approximately 6 months issuing into four measure points P1, P2, P3 and P4 of the opinion of the population of Kapisa toward the Blue Force and the Red Force on the period corresponding to our scenario (see table 1

below).

Opinions toward Red Force in the context of security decrease in summer periods. This could be explained by their high activity level as exposed in the previous section. As for the Blue Force, the global opinion value keeps decreasing Table 1: % of the population favorable with two questions at different dates along with their decreasing activities. In overall, the opinion dynamics showed by these polls results are consistent with the scenario previously established.

Model

General approach

Our model is based on the following principle: a simulation corresponds to the execution of actions (e.g. food distribution, construction of a bridge, bombing attack etc.) by actors (e.g. UN Force, terrorists or others) on a population. Individuals communicate about these actions with the others and form an attitude toward the actors.

In our model, we consider a set of actors A and a set of individuals Ind. Actors represent Forces that act in the simulation and for which we want to analyze the attitudes evolution. Each of them corresponds to a computational automaton executing its actions list specified by the above scenario. Each individual i ∈ Ind is represented by a computational agent and is characterized by its belief base recording facts he is aware of and his connections to other individuals. For each i ∈ Ind and actor ∈ A, we denote att(i, actor) ∈ R the attitude of the individual i toward the actor actor.

Beliefs about actions will be the core element in our model: attitudes and communications will be based on these beliefs. We note a(i) the belief of individual i about an action a. Each a(i) is a tuple: name(a), actor(a), coResp(a), bnf (a), payof f (a), date(a) with:

name the unique name of the action (e.g. "patrol", "suicide attack") actor ∈ A the actor who performed the action (Blue or Red Force) -coResp ∈ A the co-responsible actor of the action, if any (e.g. Blue Force will be co-responsible of "suicide attacks" performed by Red Force) bnf ∈ Ind ∪ A the beneficiary of the action, i.e. the individual or actor which undergoes the action payof f ∈ R the impact value of the action, negative when the action is harmful (e.g. attack) and positive when it is beneficial (e.g. food provision). date ∈ N the occurrence date of the action.

Attitudes are computed as the aggregation of evaluation of similar actions seized by the individual in his past. Two actions are similar if and only if they have the same name and actor. For instance, two distinct patrols done by the Blue Force in the same area are considered as similar. We call general action the meta action that includes similar actions. We denote ga(i, a) the general action corresponding to the action a according to the individual i and ga(i) the list of all general actions that i knows.

Actions can be perceived via direct perception (the agent is beneficiary of the action), actor's communication toward the population (the agent receives a message from the actors) or intra-population communication (the agent receives a message from another individual).

Attitude computation

When an agent receives a new information about an action a, it adds it to its belief base (if the action is not already present) and, possibly, communicates about it. Moreover, the agent revises its attitude toward the actor of the action. Our model of attitude construction is based on the model proposed by Fazio [START_REF] Russell | Attitudes as object-evaluation associations of varying strength[END_REF] (see section 2). In short, Fazio proposes to compute the attitude as the average of beliefs' evaluations (i.e. how much this fact is beneficial) weighted by their accessibilities (i.e. how accessible is the information in the subject's mind).

First we compute the interest of an information to estimate its accessibility and its narrative interest (whether or not to communicate the action to other agents). Second, we evaluate the action based on its payoff. Third, we compute the impact of co-responsibility, if required. Finally, we aggregate these evaluations, weighted by their accessibilities, to compute the attitude.

Interest of an action

In order to determine what to base their attitude on and what to communicate to other individuals, agents estimate a model of interest of the actions in their belief base. Following [START_REF] Brousmiche | The role of emotions on communication and attitude dynamics: an agent-based approach[END_REF], our model of interest is based on the Simplicity Theory of Dessalles [START_REF] Dimulescu | Understanding narrative interest: Some evidence on the role of unexpectedness[END_REF] which proposes to define the narrative interest N I of an information according to the emotion E and the surprise level S it causes to the individual using the following formula: N I(a) = 2 αE(a)+(1-α)S(a) where E corresponds to the personal emotional response intensity of the individual when faced to an information and the surprise level S translates the sentiment of unexpectedness felt by the individual. The parameter α ∈ [0, 1] balances these two parts.

The emotional intensity E corresponds to the emotional amplitude experienced by the individual when exposed to an event and follows a logarithmic law in conformity with Weber-Fechner's law of the stimuli. In our case, the stimuli corresponds to actions' payoff. A parameter of personal sensibility ξ ∈ [0, 1] modulates the response intensity.

The surprise S experienced by an individual when exposed to an event derives from a level of raw unexpectedness (e.g. "It is surprising that a Taliban saves a citizen"). This level is reduced by a personal reference of unexpectedness based on a personal experience (e.g. "But I have once been saved by a Taliban before").

While N I(a) corresponds to the narrative interest used as a communication heuristic, we also compute the information's interest corresponding to the accessibility of the information:

interest(a) = log(N I(a)) = αE(a) + (1 -α)S(a)
Action evaluation Fishbein and Ajzen [START_REF] Ajzen | The influence of attitudes on behavior[END_REF] advance that the evaluation of an action is weighted by the attitude toward its beneficiary. For instance, a beneficial action for my enemy is harmful for me. Therefore, we define the evaluation of an action belief as:

evaluation(a) = payof f (a) × att(i, bnf (a))
Co-responsibility In our case study, the Blue Force endorses the role of security guardian, thus they are co-responsible of all actions compromising the security of the population including Talibans' attacks from population's perspective. Thus, we introduce a co-responsibility mechanism that enables individual to attribute a fraction ρ ∈ [0, 1], parameter of the simulation, of an action payoff to the co-responsible. This mechanism occurs when an individual faces an action a in which (1) there is a co-responsible actor, (2) its impact is negative (i.e. there is no co-responsibility for beneficial actions) and (3) its evaluation is negative. In that specific case, the individual adds a belief a with actor(a) = coResp(a) and evaluation(a ) = ρ × evaluation(a).

Aggregation Let gaList(i, actor) be the list containing all the general actions performed by the actor in the belief base of agent i. The attitude att(i, actor) of the individual i toward the actor is given at each time of the simulation by:

att(i, actor) = ga(i)∈gaList(i,actor)   a(i)∈ga(i,a) evaluation(a) × interest(a, i)) |ga(i, a)|  

Experiments

In this section, we present the experimental results of our model. We aim to reproduce the results of opinion polls collected on the field using the established scenario of events that took place in Kapisa between September 2011 and September 2013. Since the polls did not asked directly the opinion toward the Red Force but "whether they represent a threat" (see section 3), we decided to take the opposite of these results as the target attitude values.

Simulation settings and initialization

We input the action sequence presented in the scenario of both Red and Blue Forces into the simulation scheduler; one tick corresponds to one day: the simulation covers the period between the first and last opinion polls in 554 ticks. The two agents corresponding to each Force will then operate their actions according to the scenario. The artificial population representing the inhabitants of Kapisa is composed of 150 agents connected by an interaction network based on a small-world topology [START_REF] Milgram | The small world problem[END_REF] with a degree of 4 (i.e. each individual has 4 neighbors in average). Before running the actual simulation, we initialize the population with a personal history for each individual and an attitude corresponding to the value given by P1. Indeed, one of our model originality resides in the fact that the attitude depends on the agent's cognitive state characterized by its beliefs and accessibility values. Thus, we must give individuals an initialization belief with a certain reach and payoff for both attitudes toward Red and Blue Forces. These beliefs represent the interactions with Forces preceding to the simulation span. Another subtle point in our model is that individuals are surprised when they Fig. 2: Simulation results compared to opinion polls witness a totally new action, resulting in an overestimation of the action's impact. In order to habituate them to certain regular actions (such as patrols, preaches, radio broadcasts etc.) we need to run an initialization scenario before the actual one in which the population is confronted to these actions, until we reach a stable point (approximately 200 ticks).

Calibration method Once the simulation is properly initialized, we calibrate the model parameters using each opinion polls results as objectives. We have four points to calibrate per Force, thus totaling 8 points of calibration. The model parameters are shared among all individuals of the population:

α the weight of emotional sensibility toward the surprise factor ξ the level of sensibility to a stimuli (i.e. payoff) ρ the co-responsibility factor of Blue Forces for harmful Red actions We also have to determine the parameters of initialization actions to attain the first point P1: one positive and one negative action per Force. To do so, we fix their payoff values (negative for the harmful action and positive for the other) and calibrate their reaches.

We define our fitness function as the sum of differences' squares between each point of the opinion poll results and its corresponding percentage of favorable individuals in the simulation. We choose to minimize this fitness using the evolutionary algorithm CMA-ES that is one of the most powerful calibration method to solve this kind of problem [START_REF] Hansen | Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES)[END_REF]. Once the fitness stops progressing over 500 iterations, we interrupt the calibration process and save the parameters. Each calibration iteration is based on the average output on over 20 simulations replica since the model is stochastic.

Calibration results

The figure 2 shows the results of our model once its parameters have been calibrated. Plain curves represent the objectives to reach that are based on the collected opinion polls results (see section 3) ; dashed curves correspond to the simulation results, with α = 0.70, ξ = 0.08 and ρ = 0.15 (as obtained by the calibration).

We can observe that the attitude dynamics tendencies are well reproduced. The average difference between results and objective points is 13.25% with a maximum of 19% for the last point. This gap between survey and simulation results could be explained by several factors in addition to the model itself: field data are generally inaccurate and capture only a limited part of reality.

First, the established scenario is based on subjective assessments of some Blue Force officers and do not capture all the military events that took place on the terrain. Adding to this, the parameters of action's models (i.e. payoffs, frequency and reach) have been assessed based on qualitative appraisal of subject matter experts since there is no scientific method to assess them.

Second, the sampling of the opinion survey could not be maintained through the survey process, due to the dynamics of the conflict : certain villages could not be accessed constantly over time due to their dangerousness. Moreover, as it was pointed earlier, the questionnaire did not directly ask the opinion toward Red Force which might increase the gap between our model outputs and the polls results.

Finally, our field data is limited to the context of military events. Even if our study concerns attitudes toward Forces in the military/security context, other events might also have influenced these attitudes such as economic or daily activities.

In view of these limitations, the reproduction of the general tendencies of attitude dynamics between each polls seems encouraging. Besides, these results have been obtained by calibrating only 3 model parameters.

Attitude Dynamics Agent based simulation enables not only to reproduce aggregated data but also to analyze micro behaviors. Figures 3 and4 below show the dynamics of population's attitudes means values between two polls along with their corresponding scenario.

The decreasing general tendency of the attitude toward the Red Force between P1 and P2 in figure 3 is due to the constant pressure activity that affects negatively the population. We can observe repercussions of each occurrence of action on the attitude dynamics. For instance, in figure 3 we can clearly see attitude decreasing toward Red Force at each IED (Improvised Explosive Device, gray blocs in the figure) and also that the first occurrence has the greatest impact since the population is surprised. Besides, we can notice that the impact of a suicide attack is much greater than other actions (mid-July 2011 and June 2013).

In figure 4, we can see that attitudes toward the Blue Force are also impacted by each of its actions. Moreover, we can notice that the curve greatly decreases when the suicide attack perpetuated by Reds occurs. This phenomenon is en-Fig. 3: Attitude mean toward Red Force between P1 and P2 Fig. 4: Attitude mean toward Blue Force between P3 and P4 abled by the mechanism of co-responsibility (see 4.2), the Blue Force is also responsible of these attacks, in a moderate way. Similarly, the attitude toward Blue Force decreases constantly since the background communication actions and patrols are not enough to counter their co-responsibility toward the pressure activity for Reds.

Role of co-responsibility

To show the impact of the co-responsibility mechanism, we have performed a new simulation with ρ = 0 (i.e. no co-responsibility). Since the initial values of attitudes toward Blue Force depend on ρ during the initialization scenario, we had to re-calibrate the model parameters. Figure 5 shows the resulting attitude dynamics. As it was predictable, the attitude toward the Blue Force constantly increases as their only potentially negative actions (ki-netic actions against the Red Force) are easily countered by communication toward the population or reconstruction actions. This is what was expected by the stakeholders when they decided to engage in Afghanistan.

The tendency of attitude toward Red Force remains the same since the coresponsibility only affects the Blue. We can notice that the simulated attitudes toward Reds is closer to opinion polls than in the first calibration. This is due to the fact that the red scenario is more simple (less action diversity).

Conclusion

We have collected information on the opinion dynamics and the events during the involvement of French army in Afghan war. These information were analyzed and processed with the support of subject matter experts. Based on these field observations, we proposed a simulation model of attitude dynamics. This model embodies both cognitive and affective components in the formation of attitude and the diffusion of beliefs. While it was designed in the context of military operations, it can be applied to civilian use: the actors can represent any kind of active social object such as political parties, institutions, companies or brands.

We also introduced a new concept of co-responsibility that reflects attitude behavior perceptible in conflict terrain. These components were aggregated through a new method that better understands the concept of attitude proposed by Fazio. We conducted a model calibration based on the collected data that showed encouraging results.

In future works, we intend to conduct deeper analysis of the field data to adapt the model and implement the simulation of different social groups that are present in this area. Moreover, we would like to implement a memory mechanism to let the agents "forget" some beliefs over time. This would be enabled for

Fig. 1 :

 1 Fig. 1: Scenario of Blue and Red Forces actions. The time points P1, • • • , P4 correspond to the dates of the opinion polls presented in the next section.

Fig. 5 :

 5 Fig. 5: Impact of co-responsibility

These opinion surveys have been ordered by the French Ministry of Defense.

Joint Command for Human Terrain Actions: in charge of civil-military actions (e.g. reconstruction) and community outreach actions (i.e. attitude influence operations) that complement conventional security operations.

instance using the Peak-End mechanism of Kahneman [START_REF] Daniel Kahneman | Experienced utility and objective happiness: A moment-based approach[END_REF]. Furthermore, we would like to add a behavioral component to enable population agents to express their attitudes through actions selection.