Jinshan Liu
email: jinshan.liu@inria.fr

Valérie Issarny
email: valerie.issarny@inria.fr

Service Allocation in Selfish Mobile Ad hoc Networks Using Vickrey Auction

Incentive scheme for stimulating service provision in Mobile Ad hoc NETworks (MANET) has been under intensive investigation due to its significance to the operation of MANET. This paper applies distributed algorithmic mechanism design and utilizes Vickrey auction for service allocation in mobile ad hoc networks. We show that our method stimulates service provision and achieves desired system-wide service allocation in spite of each agent's selfish behavior, while introducing challenges from the inherent shortcomings of Vickrey auction and characteristics of MANET. We discuss the challenges, the existing solutions for wireline networks and propose a system model for service allocation in MANET.

Introduction

The popularity of light-weight terminals (e.g., handhelds, PDAs and cell phones) with integrated communication capabilities facilitates the ambient intelligence vision of service access anytime, anywhere. Mobile distributed systems that provide access to information and services spread among autonomous devices is of paramount importance to the vision. To realize the above system, cooperation among autonomous devices is a necessity. Unfortunately, cooperative behavior implies resource consumption (e.g., battery), which is not in the interest of the autonomous devices. How to stimulate service provision in mobile ad hoc networks has thus drawn intensive research activities [START_REF] Buttyan | Stimulating cooperation in self-organizing mobile ad hoc networks[END_REF][START_REF] Marti | Mitigating routing misbehavior in mobile ad hoc networks[END_REF][START_REF] Obreiter | Stimulating cooperative behavior of autonomous devices: An analysis of requirements and existing approaches[END_REF][START_REF] Zhong | Sprite: A simple, cheat-proof, credit-based system for mobile ad-hoc networks[END_REF].

Not only do we need to stimulate service provision in ad hoc networks, we also need to allocate services in an efficient manner. More specifically, the allocation result should be a social, system-wide choice instead of an individual one. "Service", an instantiated configured system that is run by a providing organization [START_REF] Szyperski | Component Technology -What, Where and How?[END_REF], here not only refers to services like packets routing in network layer, but also to services in application layer, e.g., Web services [START_REF] Issarny | Developing Ambient Intelligence Systems: A Solution based on Web Services[END_REF]. Current directory-based service repositories (e.g., Jini) require centralized storage scheme and do not address the incentive issues of service allocation. Even with distributed service discovery (e.g., Service Discovery Protocol (SDP) for bluetooth), it is not as trivial as adding a price field in the service description record: (1) one issue is autonomous entities are normally unwilling to reveal their prices, which are considered private; [START_REF] Boulkenafed | Group Management in Mobile Ad Hoc Networks: Design, Implementation and Experiments[END_REF] another issue is selecting the least expensive service and pay the lowest price does not serve a service most economically 1 . Mechanism design, especially Distributed Algorithmic Mechanism Design (DAMD) [START_REF] Feigenbaum | Distributed algorithmic mechanism design: Recent results and future directions[END_REF], is emerging as a suitable approach for solving the above mentioned problem. DAMD addresses the design of incentive compatible mechanisms (i.e., mechanisms that result in desired system-wide outcome from selfish behavior by the system's agents) at tractable computational and communication expense. It lies in the intersection of economics science and computer science.

DAMD is suitable for Mobile Ad hoc NETworks (MANET) because: [START_REF] Baudron | Non-interactive private auctions[END_REF] MANET is distributed by nature; (2) MANET can only afford tractable computational and communication complexity. However, DAMD for MANET is challenged by MANET's resourceconstraints, infrastructureless and unpredictable nature: [START_REF] Baudron | Non-interactive private auctions[END_REF] The inherent mobility of MANET requires the designed mechanism to be robust; (2) Many existing DAMDbased mechanisms for wireline networks assume perfect connectivity between nodes, which is arguable in MANET; (3) Thin devices' limited computational and communication (C2) capability makes C2-saving a desirable property for mechanisms, as tractable complexity (i.e., polynomial computational complexity) can still be too heavy in many scenarios. Therefore, DAMD employed on wireline networks cannot be applied directly in the context of MANET, which demands specific consideration.

In this paper, we more specifically discuss application of Vickrey auction [START_REF] Vickrey | Counter speculation, auctions, and competitive sealed tenders[END_REF] for service allocation in mobile ad hoc networks. Vickrey auction is a well-known incentive compatible mechanism for solving service and resource allocation problems in multiagent systems in an efficient, distributed and autonomy-preserving manner. Design of algorithmic Vickrey auction falls into DAMD. This paper has three goals: (1) introduce DAMD as a possible tool for designing incentive schemes for MANET; (2) discuss the challenges of applying Vickrey auction in MANET; (3) propose a viable model for service allocation via Vickrey auction in MANET. The paper is organized as follows. The next section introduces mechanism design and various auctions, explains how to apply Vickrey auction as a valuable tool for optimizing service allocation in MANET and outlines the accompanying challenges. Section 3 gives existing solutions in wireline networks for each challenge, and proposes a system model for service allocation via Vickrey auction in MANET. Finally, Section 4 concludes with discussion on future work.

Mechanism Design for Service Allocation in MANET

The field of mechanism design studies how to design systems so that agents' selfish behavior results in desired system-wide goals. "Agent" here refers to a software entity representing and working for the interests of the host node. However, the game-theory literature on mechanism design neglects computational and communication complexity, which makes mechanism-design approach unpractical in a lot of settings (e.g., Internet). This is addressed by DAMD (Sect. 2.1). Distributed algorithmic auctions, especially Vickrey auction, are a valuable tool for service allocation (Sect. 2.2). After justifying Vickrey auction as the most suitable auction setting for our purpose, we detail the application of Vickrey auction to MANET with confronted challenges (Sect. 2.3).

Distributed Algorithmic Mechanism Design

In essence, Distributed Algorithmic Mechanism Design (DAMD) studies how to design incentive compatible mechanisms with tractable computation (i.e., algorithmic mechanism design [START_REF] Nisan | Algorithmic mechanism design[END_REF]) in a distributed setting such as Internet computing [START_REF] Feigenbaum | Distributed algorithmic mechanism design: Recent results and future directions[END_REF]. DAMD takes into account communication overhead in addition to the local computational burden on agents.

More formally, consider a distributed system in which there is a set of possible outcomes O (e.g., result of resource allocation) 2 . A strategy for an agent is a complete contingency plan, i.e., a plan describing what decision the agent should make under each possible situation that might occur. Each of the n autonomous strategic agents3 has a utility function u i : O → R, where u i ∈ U (U defines the set of utility functions of agents) expresses an agent's preferences over these outcomes. The desired systemwide goals are specified by a Social Choice Function (SCF) F : U n → O that maps the (actual) utility functions of agents to a particular outcome. However, each agent is usually reluctant to publicize its actual utility function, thus making it impossible to achieve any global goal.

For a given mechanism M, let S denote the strategy space of one agent, i.e., a set of strategies that can be taken by the agent, and C M (u) ⊆ S n denote all possible strategy vectors that could reasonably result from selfish behavior. The goal of mechanism design is to define a mechanism M that implements the SCF, i.e., M (C M (U )) = F (U ), for all U ∈ U n . With such a mechanism qualified as being incentive compatible, selfish behavior by agents will result in desired system-wide outcomes.

In game theory, the strategy that is always in the best interest of one agent, no matter how other agents act, is named dominant strategy. A mechanism with dominant strategy is very desirable for scenarios featuring interactions among autonomous, automated agents, compared to those without dominant strategy: (1) the behavior of agents is much simpler -following the dominant strategy regardless of other agents' behavior; (2) it saves the complex knowledge representation and logic evaluation for counterspeculating how other agents will behave. Thus, it is very desirable to have an incentive compatible mechanism with dominant strategy. The emphasis of mechanism design is put on the implementation of various types of auctions [START_REF] Nisan | Algorithmic mechanism design[END_REF], which has been an efficient means for resource allocation [START_REF] Mcmillan | Selling spectrum rights[END_REF], service assignment [START_REF] Vulkan | Efficient mechanisms for the supply of services in multi-agent environments[END_REF] and conflict resolution [START_REF] Capra | CARISMA: Context-Aware REflective mIddleware System for Mobile Applications[END_REF]. In the following, we investigate what kind of auction settings is suitable for our purpose.

Auctions

An auction consists of an auctioneer and potential bidders. The commonly seen auctions include English auction, first-price sealed-bid auction, Dutch auction and Vickrey auction.

English Auction. In English auction, the auctioneer starts with the reserve price and proceeds to solicit successively higher bids from the bidders until no one raises the bid. The highest bidder is the winner and pays the price she bids. The dominant strategy for one agent in English auction is to continuously raise its bid until it wins or it reaches the maximum price it is willing to pay for that item. A noticeable feature of English auction is that it is usually multi-round and the time and communication overhead is proportional to the difference between the starting price and the price at which the item is sold. However, it does allocate the item to the bidder with the highest valuation, who is the only bidder willing to outbid all other bidders.

First-Price, Sealed-Bid Auction. In first-price sealed auction, each bidder submits one bid in ignorance of all other bids to the auctioneer, who determines the highest bid and sells the item to that bidder for the bidding price. This kind of auction can be executed in one-round and thus is communication-saving. However, since each agent's bid is based on her private valuation and prior beliefs of others' valuations, the item is not always awarded to the party who values it most.

Dutch Auction. In Dutch auction, bidding starts at an extremely high price and is progressively lowered until a buyer claims an item by calling "mine". The winner pays the price at the current price. Dutch auction preserves maximal privacy: only the highest bid is revealed. However, like English Auction, it is multi-round, and like first-price, sealed-bid auction, one agent's bid is strategically based on its private valuations and its beliefs of others' valuation.

Vickrey Auction. Similar to the first-price sealed auction, Vickrey auction is sealed and executed in one-round. The highest bidder is the winner, but pays a price that is equal to the second-highest bid [START_REF] Vickrey | Counter speculation, auctions, and competitive sealed tenders[END_REF]. Vickrey auction has a very fundamental feature: the dominant strategy for every bidder is to bid her true valuation 4 . Thus Vickrey auction always rewards the item to the bidder who values it most, i.e., realizes SCF. From Table 1 that lists the features of the four previously mentioned auctions, we can see that only Vickrey auction and English auction both have dominant strategy and realize SCF. Furthermore, Vickrey auction only requires single-round execution. Thus, from the perspective of both economic incentive and communication overhead, Vickrey auction is the best mechanism for service allocation in MANET.

Service Allocation via Vickrey Auction in MANET

It is worth noting that although an auction hosted by a service provider does find the service seeker that values a service most, it does not contribute to our purpose of social service allocation, i.e., serving a service request least expensively, which is essentially to allocate the service request to the service provider that can do so. Therefore, auctions are hosted by service seeking agents, leading to reverse Vickrey auction: the bidder (i.e., service provider) with the lowest instead of the highest bid is the winner.

Bids submitted by service providers are determined by the cost for serving a service, which depends on factors like capabilities of a bidder (e.g., computation power), the load of a bidder (e.g., how many services it has already been serving), the service to be served and some other factors. As the cost reflects suitability of a service provider serving a service better than simply the load, auction-based service allocation is better than non auction-based counterpart because it finds the cheapest way to serve a service via economic payoff. Auction based service allocation also achieves load balancing because heavy load leads to higher bid and thus less possibility of winning the auction.

As mentioned above, service allocation via reverse Vickrey auction in MANET has three desired properties:

1. It has a dominant strategy, thus it is simple to implement. 2. The agents are motivated to bid. The winner gets a payoff which equals to the difference between its valuation and the second lowest bid; the losers lose nothing (i.e., payoff = 0). 3. The service is always allocated efficiently: service (request) is allocated to the provider that serves it least expensively.

Despite Vickrey auction's impressive theoretical properties, Vickrey auction has the following two major shortcomings [START_REF] Rothkopf | Why are Vickrey auctions rare?[END_REF][START_REF] Sandholm | Limitations of the Vickrey auction in computational multiagent systems[END_REF]: the fear of dishonest auctioneer and the reluctance of bidders to reveal their true valuation. Since it is sealed, the winner has every reason to doubt the price the auctioneer tells her to pay is actually the second highest price. Thus, fair execution of auctions needs to be guaranteed. Moreover, the valuation of goods or tasks are sensitive and private information that bidders are unwilling to reveal [START_REF] Brandt | Vicious strategies for Vickrey auctions[END_REF][START_REF] Rothkopf | Why are Vickrey auctions rare?[END_REF].

Design Issues for Service Allocation in MANET

The design of Vickrey auction for MANET is confronted with challenges posed by the limitation of Vickrey auction and the characteristics of MANET. Below, we investigate the resulting design issues that decompose into: Currency versus Reputation, regarding the remuneration types in auctions for MANET (Sect. 3.1); Fair Execution, on how to solve the problem of dishonest auctioneer, and assure fair execution of Vickrey auction; and Privacy, about how to keep the loser bids private (Sect. 3.2). In the end of this section, we propose a system model for carrying out Vickrey auction for service allocation in MANET (Sect. 3.3).

Currency Versus Reputation

All incentive schemes, including auctions implement a remuneration scheme to incentivize agents' cooperative behavior. Remuneration assumes a specific form that is called renumeration type. Digital currency and reputation are the two most common remuneration types [START_REF] Obreiter | Stimulating cooperative behavior of autonomous devices: An analysis of requirements and existing approaches[END_REF].

Digital currency is used to reward cooperative agents; each agent owns some amount of digital currency and the amount is either resident on some security module executed on some tamper resistant hardware [START_REF] Buttyan | Stimulating cooperation in self-organizing mobile ad hoc networks[END_REF] or available via some on-line bank service [START_REF] Zhong | Sprite: A simple, cheat-proof, credit-based system for mobile ad-hoc networks[END_REF]. However, the digital currency scheme does not imply any trust information 5 . Thus, the only way to punish misbehaving agents is to make them lose money (e.g., bidders' put some deposits before the auction [START_REF] Franklin | The design and implementation of a secure auction service[END_REF]).

Alternatively, reputation, which is a perception of an agent's trustworthiness, is resident on other agents based on their previous experience with it. In some systems such as [START_REF] Marti | Mitigating routing misbehavior in mobile ad hoc networks[END_REF], the node can monitor neighbors' activity via promiscuous mode. In that case, experience not only includes direct interaction experiences (e.g., participate in the same auction), but also indirect experiences (e.g., being neighbors but never interact with each other). Experience can even be extended to include other agents' experience via exchange of reputation information [START_REF] Liu | Enhanced reputation mechanism for mobile ad hoc networks[END_REF].

However, because there is no way to bid "reputation" for some service, digital currency is preferred over reputation for a Vickrey auction system. But, reputation can be a supplementary reference for a seller or bidder, e.g., bidders can avoid disreputable auctioneer.

Fair Execution and Privacy Preserving of Auctions

In traditional models of centralized auction (e.g., eBay), there exists a trustworthy party that everybody trusts. It is trivial to conduct an auction in such scenarios, when every bidder can safely submit bids to the trusted party, which determines the winner of the auction. The existence of a trustworthy party is almost impossible in MANET, which makes "fair auction execution" and "privacy preserving" problems even more severe.

The most seen approach for solving the above problems is to transform a single point of trust into a jury of trust; every jury member does not have to be as trustworthy as in traditional models. There are currently two variations of this approach: (1) Jury members can include multiple auctioneers, most of which are assumed to be trustworthy.After each bidder sends shares of their bid to each auctioneer, only a majority of the auctioneers can open the bid with threshold computation (e.g., Verifiable Secret Sharing (VSS) [START_REF] Pedersen | Non-interactive and information-theoretic secure verifiable secret sharing[END_REF]);

(2) Jury members can also include a semi-trusted third-party and an auctioneer. Fair execution of auctions and privacy of loser bids are guaranteed if the third-party does not collude with the auctioneer [START_REF] Naor | Privacy preserving auctions and mechanism design[END_REF], or with any bidder [START_REF] Baudron | Non-interactive private auctions[END_REF].

Executing the above secret sharing protocols requires expensive cryptographic operations, which can hinder the deployment on thin devices. Elliptic Curve Cryptography (ECC) [START_REF] Miller | Uses of elliptic curves in cryptography[END_REF] is an alternative to establish public keys, like RSA and DSA. The attraction of ECC is that significantly smaller keys can be used in ECC than other systems like RSA and DSA, but with same level of security (e.g., 160-bit ECC and 1024-bit RSA). Hence, ECC is preferable for wireless communication due to its bandwidth saving with smaller key size and smaller digital signature [START_REF] Wiener | Performance comparison of public-key cryptosystems[END_REF].

An alternative way to speed up cryptographic operations on mobile devices is to introduce some dedicated hardware for the operations. Compared to the software approach, it is much faster and more physically secure. Low-end chips such as CDL-82 TM has been planted into PDAs and biometric verification devices 6 . Additionally, dedicated hardware can also function as "nuglet counter" as mentioned in [START_REF] Buttyan | Stimulating cooperation in self-organizing mobile ad hoc networks[END_REF]. The plugging of custom hardware into computation-bounded mobile devices is proving to be a viable approach.

Service Allocation System

Having discussed the issues for applying reverse Vickrey auction for service allocation, we present a system model for conducting such auctions in mobile ad hoc networks. Our approach is essentially based on VSS and uses both remuneration schemes -digital money and reputation. Below is how reverse Vickrey auction for service allocation is carried out after the agents have formed a neighborhood via group management [START_REF] Boulkenafed | Group Management in Mobile Ad Hoc Networks: Design, Implementation and Experiments[END_REF].

Service Discovery. For discovering services, we adopt a distributed "pull" multi-hop service discovery scheme. Every service seeking agent can advertise its request, which includes information of service properties (e.g., functional and QoS properties), the auction protocol (Vickrey auction in our case) and an optional list of jury members 7 , by broadcasting it to peers bounded by number of hops. Jury members are chosen by the service seeker according to their reputation of being trustworthy, and are paid by the service seeker for their service 8 .

Reputation Checking. On receiving a service request, each interested service provider first checks whether a jury list is included in the request. If the jury list is absent, potential bidders check whether the service seeker is trustworthy enough for revealing their bids. If the jury list is provided, potential bidders check whether at least two thirds of the members are trustworthy enough for being a jury member because, as to be stated in the next subsection, the auction protocol we use tolerates up to one third of the total shares to be faulty. Therefore, providing a jury list normally increases the possibility of bidding from bidders, which is to the interest of the service seeker.

If reputation checking is passed, potential bidders execute conformance checking with respect to service properties. And, if it does satisfy the request, it estimates the resource consumption for serving such a service. Thereupon, it calculates a bid based on the estimated resource consumption [START_REF] Liu | QoS-aware service location in mobile ad hoc networks[END_REF], and thus how much it wants to get paid for that service.

Service Allocation. If the jury list is absent, the bidders submit the bids directly to the service seeker and auction is conducted in a traditional centralized way. Alternatively, if the jury list is provided, each bidder conducts a secret sharing scheme and submits the shares of the bid to the jury members. The resolution of winner and the price to be paid is done among jury members with an elegant protocol proposed by Harkavy. et. al. [START_REF] Harkavy | Electronic auctions with private bids[END_REF], which: (i) preserves privacy of bids and anonymity of losers by only revealing the winner and the price to be paid; (ii) tolerates up to one third of missing or faulty shares; and (iii) introduces affordable computation cost. The winner will then get paid with the second lowest bid after completion of service. After each auction, any participant including service seeker, jury members and service provider can update the reputation of any other according to its degree of satisfaction after the auction (e.g., if it is convinced that the auction is fair, or it suspects of any dishonesty). And, the newly update reputation serves as a reference for future encounters.

Note that bidders can have different strategies for submitting bids. An aggressive provider can submit bids to more service requests than it can serve if it wins all the auctions, while a less aggressive provider makes sure that it will be able to serve all the service bids if it wins them all. The strategy very much depends on the current workload of the bidder and the punishment for defaulting a winner bid. As the amount of bids are partially determined by the load of the bidder, it is also affected by the bidder' bidding strategies. The bidders' strategies and their effect on bids is part of our future work.

As for the impact of mobility on our system model, one obvious observation is that our system is robust to leaving of up to one third of the jury members during the auction. However, since our model assumes connectivity among service seekers, jury members and bidders, the auction may be interrupted if mobility disrupts the connectivity.

The overhead of our model lies in the execution of bid resolution protocol. Assume the number of bidders is n, number of jury members is m, the threshold number is t (i.e., t jury members can not reveal the bids and 3t < m), and the number of bits to represent a bid is V, the communication cost for the auction protocol is O(t 2 * m * n * logV ) [START_REF] Harkavy | Electronic auctions with private bids[END_REF].

Conclusion and Future work

In this paper, we have discussed issues that arise during design of a Vickrey auction system for service allocation in MANET. The motivation of Vickrey auction for MANET lies in its value for efficient service allocation in a social way. We have shown that although intensive study has been done to solve the problems raised by the limitation of Vickrey auction, additional challenges are confronted due to the infrastructureless and unpredictable nature of MANET. We further have proposed a system model for service allocation via Vickrey auction in MANET.

A possible extension of the current work is to base service allocation on offered QoS together with the amount of bids. One instinctive approach is to base selection on bid divided by client's QoS-dependent utility. But it is problematic because QoS dimensions require normalization since they are in different units [START_REF] Liu | QoS-aware service location in mobile ad hoc networks[END_REF]. Standard normalization among bids makes bidder's normalized bid dependent on other bids, which makes the existence of dominant strategy doubtful. Additionally, the amount of payment needs careful consideration since the winner is not always the lowest bidder.

In addition to bidders' strategies, as mentioned in the end of last section, our future work also includes adding deposit support for enforcing non-repudiation during the auction. This is necessary to guard against winners' defaulting behaviors and enforce honest participation of the auction. Note that the deposit in our work is provided by the service providers (i.e., sellers), contrary to buyers in normal cases. Another extension is to add support for ensuring the fair exchange of service and digital currency between the service seeker and the service provider.

Table 1 .

 1 Various Auction Settings

	Auction Type	Communication Com-	Existence of Dominant	Optimal	Item
		plexity	Strategy	Allocation
	English Auction	Multi-Round	Yes	Yes
	First-price, Sealed Auction	One-Round	No	No
	Dutch Auction	Multi-Round	No	No
	Vickrey Auction	One-Round	Yes	Yes

As analyzed in Sect.

2.2, it is actually a first-price, sealed-bid auction, in which bidders' bids are affected by their belief of peers' valuation.

We rely mostly on[START_REF] Feigenbaum | Distributed algorithmic mechanism design: Recent results and future directions[END_REF] for definitions and notation.

Since even random behavior can be considered as one kind of strategy, every agent is a strategic agent, strictly speaking.

Readers are referred to[START_REF] Varian | Economic mechanism design for computerized agents[END_REF] for the proof.

You cannot tell one person is trustworthy simply because he/she is rich.

Readers are referred to http://www.cdlusa.com/press/press.shtml for further details.

Absence of this list indicates that the auction is to be conducted in a "traditional" mode, i.e., every bidder submits its bid directly to the service seeker.

The allocation of jury member service can also be conducted via Vickrey auction, except that the very first juries for finding jury members may have to be located with the help of some third party.

Acknowledgments

This research is partially supported by the European Commission via IST OZONE (New Technologies and Services for Emerging Nomadic Societies) project 9 (IST-2000-30026).


Yanita Andonova
email: yanita.andonova@gmail.com

The Geopolitical approach to creativity

Keywords: geopolitics of management, ideology, political economy of creativity

What are the links between the geopolitics of management and creativity? To answer this question, we have carried out a research project based on a corpus analysis of the Harvard Business Review (1922Review ( -2018)), a reference journal in the field of management1 . The aim is to study how representations of creativity have evolved in response to geopolitical issues from the early 20th century until the start of the 21st. Particular attention is given to the concepts of ideology and democracy.

Our article explores the links between geopolitics, management, and creativity in the wake of Armand Mattelart's research into information and communication sciences (1992), and Paul Ricoeur's work in political philosophy (1997). In an earlier work, Ricoeur proposed rethinking the intersection between ideology and utopia in the social imagination, and their mutual exchange. It seems, indeed, he wrote, "that we always need utopia in its fundamental function of contestation and projection into a radical elsewhere, in order to carry out an equally radical critique of ideologies. But the opposite is true. Everything happens as if, to cure the utopia of the madness into which it increasingly risks sinking, it was necessary to appeal to the healthy function of the ideology, to its capacity to give a historical community the equivalent of what we called yesterday a narrative identity" (Ricoeur, 1984, p.63-64). Armand Mattelart observed the post-1945 displacement of the "ideology of progress" by the "ideology of communication" and analysed international communication. He demonstrated how the term "propaganda" was gradually replaced by the more acceptable term of "psychological warfare" in a context where ANDONOVA Y. ( 2022), "The Geopolitical approach to creativity", pp.111-114, in Conference Proceedings "Craftwork, design, creativity : between tradition and contemporaneity" 8-10 June 2022, Paissii Hilendarski University -Plovdiv (Bulgaria) https://hal.science/hal-04139653/document pp.111-114

"the foundations of the synergy of private enterprise-Pentagon, industrial production-military research, university research, and the need for national security are laid" (Mattelart, 1992, p.102). It is in this specific geopolitical and intellectual context of the beginning of the Cold War that we have identified the emergence of the reference to creativity, research into which has grown exponentially in the United States since 1950. More precisely, it is in the American management discourse of the mid-20th century that we find the first signs of the appeal to creativity. How can we place the emergence of this hegemonic discourse on creativity in the professional world? What are its origins?

Based on this observation, we decided to conduct a study into the evolution of representations of creativity in American management thought through a corpus analysis of the HBR. Published since 1922, it is one of the world's oldest journals in the field of management, and an undeniable opinion leader in American publishing [START_REF] Spector | The Harvard Business Review goes to war[END_REF] Several significant results emerge from this study. We will here discuss only a few of them, more specifically those related to what we call the "geopolitics of management of creativity". -The first period is that of the immediate post-war years when the concept of creativity emerges in articles as an individual aptitude, and a powerful tool against the increasing authoritarianism and conformity of the time. In the midst of the Cold War, in the 1950s, creativity was presented in the pages of the HBR as synonymous with patriotism, market power and effective management. These early representations were based on the importance of individual creativity traits, fully justifying the period's fascination with its psychological dimension.

-The early 1960s saw the gradual reduction in the promotion of the virtues of creativity, as opposed to the vices of conformity. Between the mid-1960s to the 1980s, creativity became synonymous with social criticism; as a free-thinking model, creativity would henceforth be closely linked to innovation. As a counter to the "Japanese miracle", innovation and creativity were increasingly referred to, and became central to management styles.

  . Launched 14 years after the founding of the Harvard Business School, it played a central role in extending the authority and influence of the Business School across the Atlantic. It has published work by numerous prestigious researchers, practitioners, experts, business leaders, and consultants. The attraction of the HBR means it has a huge network of potential authors and case studies. The textual analysis we implemented was based on a keyword search and a thematic content analysis of all HBR articles covering the 96 years of publications.

Figure 1 :

 1 Figure 1: Examples of article titles on creativity published in the HBR

The results of this research are published in an article in the journal Questions de communication (ANDONOVA,

2021): http://journals.openedition.org/questionsdecommunication/25660


Jinshan Liu
email: jinshan.liu@inria.fr

Valérie Issarny
email: valerie.issarny@inria.fr

Enhanced Reputation Mechanism for Mobile Ad Hoc Networks

Interactions between entities unknown to each other are inevitable in the ambient intelligence vision of service access anytime, anywhere. Trust management through a reputation mechanism to facilitate such interactions is recognized as a vital part of mobile ad hoc networks, which features lack of infrastructure, autonomy, mobility and resource scarcity of composing light-weight terminals. However, the design of a reputation mechanism is faced by challenges of how to enforce reputation information sharing and honest recommendation elicitation. In this paper, we present a reputation model, which incorporates two essential dimensions, time and context, along with mechanisms supporting reputation formation, evolution and propagation. By introducing the notion of recommendation reputation, our reputation mechanism shows effectiveness in distinguishing truth-telling and lying agents, obtaining true reputation of an agent, and ensuring reliability against attacks of defame and collusion.

Introduction

The pervasiveness of lightweight terminals (e.g., handhelds, PDAs and cell phones) with integrated communication capabilities facilitates the ambient intelligence vision of service access anytime, anywhere. This necessitates interactions between terminals belonging to different authorities, which are marginally known or completely unknown to each other. Trust management to enable such interactions has thus been recognized as a vital part of mobile ad hoc networks (MANET), which features lack of infrastructure, openness, node mobility, and resource scarcity (e.g., network, energy and storage space) of composing lightweight terminals.

In closed networks, trust establishment is managed by an authentication mechanism that assigns roles to agents. By agent, we mean a software entity working for and representing a node in MANET; each agent also has some reachable neighbor agents named peers. In an open environment such as MANET, fixed role assignment has to be be replaced by dynamic decisions. An important factor affecting the decision making is an agent's reputation.

Reputation assessment requires knowledge, information and evidence about the evaluated agent, which can be derived from an agent's own experiences. However, openness implies significant opportunities of meeting with strangers an agent has never encountered before. Furthermore, more accurate estimation of an agent's reputation becomes possible with sharing of reputation information among peers. Reputation mechanism has been widely used and implemented in electronic market places [START_REF] Miller | Eliciting honest feedback in electronic markets[END_REF][START_REF] Grandison | A survey of trust in internet applications[END_REF] and online communities [START_REF] Zacharia | Trust management through reputation mechanisms[END_REF]. For example, visitors at "amazon.com" or eBay usually read previous customers' reviews and feedbacks before deciding whether to make transactions.

However, the design of a reputation mechanism is faced by a number of challenges, including: (i) the "free-rider" problem, i.e., agents do not share reputation information with peers; and (ii) the honest elicitation problem, i.e., agents may report false reputation information. There are multiple reasons for agents to be reluctant to report evaluations or to do so honestly [START_REF] Miller | Eliciting honest feedback in electronic markets[END_REF]. Agents may withhold positive evaluations if a seller's capacity is limited, e.g., wise parents are reluctant to reveal the names of their favorite baby-sitters. Agents may be reluctant to give positive recommendations because it lifts the reputation of the evaluated agent, which is a potential competitor. Agents may wish to be considered "nice", or be afraid of retaliation for negative feedbacks. And last but not least, the reputation information agents provide only benefits other peers.

Therefore, it is necessary to build a reputation mechanism to enforce both active reputation information sharing and truthful recommendation elicitation, which are necessary for a reputation system to operate effectively [START_REF] Resnick | Reputation systems[END_REF]. Our target reputation mechanism aims to defend against the following three kinds of attacks:

-Inactivity: This refers to agents' free-ride activities by not sharing reputation information with peers. -Defame: This refers to agents' activities of propagating a victim's reputation that is lowered on purpose. -Collusion: This refers to agents' activities of propagating good reputation to promote each other.

Hence, the desired properties of a reputation system for MANET are:

1. Valid: The system is effective in the sense that agents are able to distinguish honest from dishonest agents through the reputation system. 2. Distributed: The system should not assume access to any trustworthy entity (e.g., Certificate Authority), or centralized storage of reputation values. 3. Robust: The system is robust to the attacks listed above. 4. Timely: The system should be dynamic and be able to reflect the trustworthiness of an entity in an up-to-date manner. 5. Resource-saving: The reputation system should take into account the limited computation power and storage space of each terminal in MANET.

Existing reputation systems either do not address the aforementioned incentive problems (e.g., [START_REF] Buchegger | Performance analysis of the CONFIDANT protocol[END_REF][START_REF] Michiardi | CORE: a collaborative reputation mechanism to enforce node cooperation in mobile ad hoc networks[END_REF]), or depend on some (centralized) trustworthy entity (e.g., [START_REF] Miller | Eliciting honest feedback in electronic markets[END_REF][START_REF] Jurca | An incentive compatible reputation mechanism[END_REF]). Our approach, which is targeted at mobile ad hoc networks, does not depend on any trustworthy entity or any centralized reputation storage, and possesses the aforementioned desired properties. Our contribution includes: (1) a reputation model that incorporates two dimensions, time and context, which captures reputation's time-sensitivity and context-dependence; (2) a simple yet effective reputation mechanism that enforces active and truthful reputation information sharing; (3) validation of the effectiveness and robustness of the proposed reputation mechanism via simulation tests. Our work targets service provision among agents in MANET. The service notion here is general1 , referring to not only services like Web services [START_REF] Issarny | Developing Ambient Intelligence Systems: A Solution based on Web Services[END_REF], packet forwarding services [START_REF] Michiardi | CORE: a collaborative reputation mechanism to enforce node cooperation in mobile ad hoc networks[END_REF][START_REF] Buchegger | Performance analysis of the CONFIDANT protocol[END_REF], but also activities like providing information (e.g., providing cuisine recipes) in online discussion forums.

In the following, Section 2 gives definitions and properties of reputation. Section 3 describes our reputation model, together with related mechanism supporting reputation formation, evolution and propagation. Section 4 presents results of simulation tests. Section 5 surveys related work. Finally, the paper finishes with conclusion and future work.

Reputation

Reputation is always associated, and often confused with trust. Therefore, in order to have a precise view of reputation, it is necessary to grasp the meaning of trust. Trust is a complex concept relating to belief in the honesty, truthfulness, competence, reliability, etc., of the trusted person or service [START_REF] Grandison | A survey of trust in internet applications[END_REF]. Precisely defined, "...trust (or, symmetrically, distrust) is a particular level of the subjective probability with which an agent assesses that another agent or group of agents will perform a particular action, both before he can monitor such action (or independently of his capacity ever to be able to monitor it) and in a context in which it affects his own action" [START_REF] Gambetta | Can we trust trust? In: Trust, Making and Breaking Cooperative Relations[END_REF]. Trust towards an agent can been seen as a prediction on that agent's future action. An important factor affecting the prediction is then the reputation of the agent.

Defining Reputation

Mui et al. define reputation as "perception that an agent creates through past actions about its intentions and norms" [START_REF] Mui | A computational model of trust and reputation[END_REF]. This definition is precise except that it does not reflect the fact that reputation of an agent is created from the point of view of other agents. An agent can affect its own reputation by acting honestly or the other way, but it is unable to decide its reputation. To emphasize the "passive" property of reputation, we define reputation as follows:

Reputation of an agent is a perception regarding its behavior norms, which is held by other agents, based on experiences and observation2 of its past actions.

The reputation assessment of an evaluated agent by an evaluator agent requires collecting related evidences beforehand. The sources of reputation include: (i) The evaluator's own interaction experiences with the evaluated agent; if the evaluator has first-hand experience of interacting with the evaluated agent, the interaction histories can serve as a strong reference for reputation evaluation. (ii) Recommendation from peers who have interacted with the evaluated agent before; note that recommendations of recommending agents are based on the agents' own experiences only, and do not include recommendations obtained from peers. This is necessary to prevent double counting that leads to rumors.

The node mobility and openness of MANET augment the opportunities for nodes to interact with nodes they never encountered before. This increases the agents' reliance on the latter source of reputation (i.e., recommendations from peers).

Properties of Reputation

Trust is widely deemed subjective [START_REF] Misztal | Trust in Modern Societies[END_REF][START_REF] Marsh | Formalising Trust as a Computational Concept[END_REF]. Reputation, a perception of the trustworthiness of an agent based on experiences and recommendations, is also subjective [START_REF] Mui | A computational model of trust and reputation[END_REF] -because the same behavior can cause different impressions on different agents. It implies that one agent is likely to have different reputations in the view of different peers. We denote Rep a (o) as the reputation of the agent o, from the point of view of agent a. We represent reputation with a numeric value in the range [-1.. + 1]. The value of reputation ranges from completely untrustworthy (-1) to completely trustworthy (+1). The larger the value is, the trustworthier the agent is. One value in the range that is worth mentioning is ignorance, which describes the reputation of agents about whom the evaluator has no knowledge. Ignorance bears the value 03 . Also we define very trustworthy (0.8), trustworthy (0.2), untrustworthy (-0.2) and very untrustworthy (-0.8). These labels do not stand for the only possible values of reputation. Instead, they are used to attach semantic meanings to numeric values. For example, if an agent's reputation value is 0.5, it is then considered to be between very trustworthy and trustworthy.

Reputation is also context-dependent [START_REF] Cahill | Using trust for secure collaboration in uncertain environments[END_REF][START_REF] Mui | Notions of reputation in multi-agents systems: A review[END_REF]. For example, David enjoys a reputation of being a very talented painter, but he may not have as high reputation as a cook. So context is an important dimension for reputation.

Reputation is also dynamic -disreputable agents should be able to improve their reputations by acting honest; reputable agents' reputation should get lower if they become deceitful. Dynamics of reputation is also reflected by its timeliness: reputation is aggregate in the time scale by taking into account recent behavior and past histories. Hence, time is also a necessary dimension for reputation.

In the next section, we present our reputation model to depict the aforementioned properties together with associated mechanism of reputation formation, evolution and propagation.

Reputation Model

To build a reliable reputation mechanism that enforces reputation information sharing and honest recommendation elicitation, our model includes the following elements:

1. Separate reputation for expertise (providing good service) and reputation for helpfulness (providing fair recommendation), respectively denoted as service reputation (SRep) and recommendation reputation (RRep). 2. Agents derive the SRep of another agent according to their experiences (SExp) and recommendations (Rec) of peers whom they consider trustworthy in service recommendation; the trustworthier a peer is, the more weight its recommendations are assigned. 3. Reputations are both timely (i.e., evolve with time) and dynamic (i.e., adjust with behaviors); especially, recommenders' RRep are adjusted according to the SRep value of the recommended agent. 4. Agents exchange reputation information, but only with peers they consider helpful (i.e., with good RRep).

The above elements motivate truthful recommendations because untruthful and inactive recommendations lead to low RRep and thus loss of peers' recommendations; peers' recommendations are an important knowledge source for evaluating an agent's SRep, especially a stranger's SRep.

Reputation Definition

Given reputation's properties of being time-sensitive and context-dependent, an accurate reputation model needs to capture the two dimensions by integrating them seamlessly into reputation's definition, formation, evolution and propagation.

Time-sensitive Reputation. Reputation builds with time. A reputation at time t can be very different from the reputation at another time t . With respect to the time dimension, we denote reputation of agent o in the view of agent a at time t as Rep a (o) t . Reputation is aggregate in the sense that it integrates peers' recommendations and the evaluator's own experiences, which are also aggregate. The weights assigned to recent behavior and past histories decide how fast the reputation builds up. For example, if recent behavior is assigned a very high weight, an agent's reputation tears down very fast after a few misbehaviors. We assign more weight to recency, as suggested by the results of psychological studies in [START_REF] Karlins | Persuasion, how opinion and attitudes are changed[END_REF] and empirical studies of ebay feedback mechanism [START_REF] Dellarocas | The digitization of word-of-mouth: Promise and challenges of online feedback mechanisms[END_REF], by adopting a parameter named fading factor ρ e :

Rep a (o) t = Rep a (o) t * ρ t-t e + New Behavior * (1 -ρ t-t e ) .
(

) 1
Value of ρ e falls into range [0..1]: the lower value ρ e has, the more quickly histories are forgotten. When ρ e equals 0, histories are completely forgotten; while when ρ e equals 1, the oldest history is forever remembered. This formula will be substantiated in the evolution of reputation ( §3.3). The representation of reputation assumes a single value with a timestamp stating the time of formation. More information is available if more history records (e.g., the last 10 reputation values) are kept. However, it consumes more space. Our representation with a single timestamped value saves storage space, which is a scarce resource for light-weight terminals, while still reflecting the time-sensitivity of reputation.

Context-dependent Reputation.

As reputation is context-dependent, it is necessary to integrate context as a dimension into reputation. As stated, SRep a (o) t in context C can be derived by information (i.e., a's experience and other peers' recommendation) in the context of C4 . But, there are cases when there is no or not enough information in the context of C, but there are plenty in a related context of C . It is good practice to be able to derive reputation from these related evidences. But, this is challenged by the question of how to capture the relevance of two contexts. This can be measured by the distance between two contexts, which is a quantitative parameter for describing the relation between the two contexts.

Context itself is a multi-dimensioned concept, it can include factors such as, importance and utility of a service [START_REF] Marsh | Formalising Trust as a Computational Concept[END_REF] (e.g., transactions dealing with 10 euros vs. transactions of 10 thousand euros), service category (driving a car vs. flying a plane), and so on. We limit the context to service category in our work, which leads to the question: how to measure the distance given two service categories? For example, assuming an agent provides excellent service in providing cuisine recipes, but we need to know whether it is also as good in giving diet tips. The question becomes how far it is between providing cuisine recipes and giving diet tips.

The comparison of services can done in a syntactic way, e.g., comparison of interfaces, attributes and so on; or in a semantic way. The former is managed by comparing service signatures. The latter is currently undertaken by the Semantic Web activity of W3C5 , which proposes languages for service description such as Resource Description Framework (RDF), and Web Ontology Language (WOL). The DARPA Agent Markup Language (DAML), an extension of XML and RDF, is able to provide sophisticated classification and property definition of resources. We thus make use of an ontology tree of services using DAML-S6 , with each node in the tree representing a type of service. Each node is a subcategory (subclass) of its parent node. To save space, we assume each agent is able to obtain a part of the ontology tree that defines the services it is interested in. Given two nodes in the tree, the distance of the two nodes is defined as the least number of intermediate nodes for one node to traverse to another node. For example, in Fig. 1, service s1 and s2 has a distance of 3. Thus, reputation on context C can be calculated as:

SRep a (o, C) t = C ∈T reea SRep a (o, C ) t * ρ |C -C| c C ∈T reea ρ |C -C| c . ( 2
)
Similar to (1), ρ c is a fading factor reflecting an agent's reliance on contextrelated reputations. When ρ c equals 0, it means the agent does not consider context-related reputations; while when ρ c equals 1, the agent takes into account all context-related reputations, all of which have the same impact factor no matter how related or unrelated they are.

In the following, we denote SRep of agent o held by agent a at time t as SRep a (o) t , instead of SRep a (o, C) t for simplicity of denotation, except during discussions of context-dependent reputations. However, it always applies that reputation in a certain context can be derived from reputation in other related contexts according to Equation (2). Table 1 summarizes the notations we have introduced so far. Having integrated time and context dimensions into our reputation model, we explore the related mechanism supporting reputation formation, evolution and propagation.

Reputation Formation

Reputation formation is implemented by the following components running on each node: an experience manager, a recommendation manager and a reputation manager.

Experience Manager

The experience manager is in charge of recording the previous experiences of service provision with other peers. The records include the service category (i.e., context C ), the timestamp of last experience (t), and an aggregate value of experience (i.e., SExp a (o, C) t ). The aggregation process of experience value will be further explored in Sec. 3.3.

Recommendation Manager

The recommendation manager implements three functions: (1) storing recommendations from other peers, (2) exchanging reputation information with other peers, and (3) managing a table of RReps of recommenders.

Recommendations from peers regarding an agent's reputation need to be combined together by some means. Dynamic Weight Majority (DWM) [START_REF] Kolter | Dynamic weighted majority: A new ensemble method for tracking concept drift[END_REF] is a learning algorithm for tracking concept drift, which predicts using a weightedmajority vote of "experts", and dynamically creates and deletes experts in response to changes in performance. Our approach tracks "an agent's reputation" by consulting recommendations (votes) from peers (experts), and dynamically changes their recommendation reputation according to their prediction accuracy. We do not delete peers from the recommender list, however, but we ignore a peer's recommendation if its RRep falls below some threshold value.

Reputation Manager

The reputation manager administers and calculates the SRep of a peer, taking into account inputs from both experience manager and recommendation manager. Reputation manager assigns different weights to experiences and recommendations, namely, greater weight for its own experience and less weight for recommendations from peers. This is due to the reason that agents tend to rely on their own experience more than on other peers' recommendation, as suggested by experimental studies of Kollock [START_REF] Kollock | The emergence of exchange structures: An experimented study of uncertainty, commitment, and trust[END_REF].

Consider agent a has recommendations regarding agent o from a group of peers P ; the peers considered untrustworthy in service recommendation (i.e., with low RRep) have been excluded from P. We get the following formula for SRep evaluation:

SRep a (o) t = α * SExp a (o) t + (1 -α) * p∈P (RRep a (p) * Rec p (o)) p∈P RRep a (p) . ( 3
)
where α is a parameter that reflects the agent's degree of reliance on its own experience. As discussed above, usually α > 0.5.

Reputation Evolution

After every interaction, agents can give a score of satisfaction for the interaction. The score of satisfaction for a service in real world is so subjective that it can depend on factors such as provided service quality, service quality expectation, environment (place, weather) and even mood. In order to evaluate subjective degree of satisfaction, we apply a method of quantifying degree of satisfaction based on the Quality of Service (QoS) 7 an agent a receives from another agent o.

Given n dimensions of QoS (e.g., availability, service latency) d i (i = 1..n) which agent a cares about, a states in its request (b 1 , b 2 ,..,b n ) in which b i is the value (either minimum or maximum) for dimension d i . As a result of the service, the quality of service that a receives is represented by (r 1 , r 2 ,..,r n ), in which r i is the value for dimension d i . The degree of satisfaction of this interaction (sat a (o)) can thus be obtained by:

sat a (o) = 1≤i≤n π(r i , b i ) * w i . ( 4
)
where π(r i , b i ) is a function to calculate one-dimensioned degree of satisfaction with respect to requested and obtained QoS. It can take the following forms:

1. π(r i , b i ) = r i /b i
when dimension i is quantitative and stronger with bigger values, for example, availability8 . 2. π(r i , b i ) = b i /r i , when dimension i is quantitative and stronger with smaller values, for example, latency.

π(r i , b

i ) = 1 -(r i ⊗ b i )
when dimension i is qualitative and bears boolean values, for example, confidentiality9 . 4. for dimensions whose value space is literals (e.g., level of service can have values of deterministic, predictive and best-effort), literals can be ordered from weak to strong and assign numeric values accordingly10 .

In the above equation, w i refers to relative importance of a dimension to an agent (e.g., availability may be more important than latency to an agent) as defined in [START_REF] Liu | QoS-aware service location in mobile ad hoc networks[END_REF].

Experience Update

With the newest interaction, agents can update their experience value with each other. Similar to (1), updating of agent a's experience of agent o at time t (denoted as as Exp a (o) t ) is as follows:

SExp a (o) t = SExp a (o) t * ρ (t-t ) + sat a (o) * (1 -ρ (t-t ) ) . ( 5
)
where t is the timestamp of last experience formation.

Reputation Update

With a new interaction, agent can then update the reputation value of the other according to (3), taking into account the newly updated experience.

Recommendation Update

Reputation varies with time. Hence, an agent's recommendation of another agent's trustworthiness also varies with time. It is thus possible for an agent a to receive recommendation from the same peer p regarding agent o (i.e., Rec p (o)) again. It is necessary for agent a to update Rec p (o) with the new recommended value. Note that we do not apply (1) here because recommendations from peers (which is supposed to be based on their SRep) already take into account the past behaviors.

Recommendation Reputation Update

With a new experience available, agent a can update the RRep of the recommender p who has recommended the newly interacted peer o.

Let us denote the difference between the newest experience value and the recommended value being dif f = |Rec p (o) -SExp a (o)|. For an honest peer p, we have Rec p (o) = SExp p (o). As stated above, reputation is subjective, but we argue that it is not arbitrary, i.e., although same kind of behavior may be of different experience to different agents, we do not expect the experience to be very contrastive. Therefore, similar to each agent's definition of threshold of trust and distrust, we propose definition of a threshold of recommendation tolerance for each agent, which defines the maximal tolerance of agent for recommendation bias (denoted δ a in the following). The value of dif f reflects the accuracy of recommendations, which needs to be normalized:

dif f = 1-dif f δa .
Then the recommendation reputation is updated as follows:

RRep a (o) t = RRep a (o) t * ρ (t-t ) + dif f * (1 -ρ (t-t ) ) . (6)
It can be seen that with false recommendation (i.e., negative dif f ), the RRep tears down with time. In order to make it possible for a disreputable agent's RRep to improve, we supplement the equation with an update method when RRep a (o) is already below σ a , i.e., RRep a (o

) t = σ a + + dif f * ρ (t-t )
, where σ a is an agent-defined reputation threshold value for being considered trustworthy in service recommendation, and is a small positive value. With our reputation evaluation as shown above, it is possible that an honest recommender whose "taste" is very different from the evaluator agent a (i.e., dif f > δ a ) is mistaken as a dishonest agent. This does not affect our model's validity because those agents' recommendations are of little value to agent a anyway. The power of our reputation system to deter inactivity lies in the dynamics of agents' behavior (e.g., trustworthy agents become deceitful) . If an agent never recommends (i.e., never exchanges reputation information with other peers), its RRep will remain as ignorance. Although ignorance bears the value of 0, it is highly possible that many agents are reluctant to exchange reputation information with agents whose RRep bears the value of 0 (it is not considered trustworthy either way). If an inactive agent did recommend but stays lazy after, it is likely that its recommended agents change their behavior, which makes its recommendation inaccurate and its RRep low. Therefore, the only way to maintain decent RRep is to recommend actively and honestly.

Reputation Propagation

For every some period 11 , the recommendation manager tries to contact peers -preferably the agents with good RRep -for reputation information exchange. In the mean time, if a recommendation manager receives a recommendation exchange request from a peer, it will first check the requester's RRep. The exchange proceeds only if the requester's RRep is above the agent-defined threshold value.

Reputation Mechanism Evaluation

In order to evaluate the effectiveness of our reputation mechanism to help agents distinguish honest and dishonest agents, and interact with unfamiliar agents, we carry out three sets of simulation tests.

Experiment Setting

Our experiment is set up with 100 agents including:

1. Agents A: it includes 30 agents which are trustworthy in both service provision and recommendation. 2. Agents B : it includes 30 agents which are trustworthy in service provision but untrustworthy in recommendation. 3. Agents C : it includes 40 agents which are untrustworthy in both service provision and recommendation.

We track agents' reputation in nRound rounds. For each round, nInt * 2 agents are randomly selected to interact with each other (before the interaction happens, they evaluate each other's SRep to decide whether to have the interaction); and nRec * 2 agents are randomly picked to exchange recommendation (similarly, they evaluate each other's RRec to decide whether to exchange).

RRec vs. SRec

The first experiment aims to show the advantages of having separate reputation for service provision and service recommendation. We set nRound = 100, nInt = 30, and set nRec to 5, 10, 15,..,50. We are interested in the number of resulting mistakes during the interactions. A mistake occurs when one agent misjudges another agent and mistakenly interacts with an untrustworthy agent or avoids a trustworthy agent. To simulate the openness of the network, every agent evaluates another peer only by the recommendations obtained from its peers (otherwise most of the interactions are between agents who have encountered each other before). Figure 2 shows the different number of mistakes occurred with or without using RRep in the last 50 rounds 12 .

We can see from the figure that, with increasing exchanges of reputation information, mistakes are decreasing for both cases. However, mistakes are less with the use of RRep, due to the impact of 30 agents (Agents B ) which are honest in service provision but deceitful in recommendation. And with full exchange of reputation information (i.e., nRec=50, which means in each round, each agent exchanges reputation information with another agent), the number of mistakes decrease from 507 to 172 out of a total of 3000 interactions.

Defense against Dynamic Behaviors

The second experiment aims to show the robustness of our reputation mechanism against dynamic behaviors of agents (e.g., some honest agents become deceitful). It exhibits the power of our mechanism to incentivize active reputation information exchange.

nRound is set to 500. In order to simulate the behavior dynamics, it is set that at round 50, agents B become honest in service recommendation and agents A become inactive and do not exchange reputation information with peers. We benchmark the average RRep of agents A, which indicates the trustworthiness in service recommendation of agents A in the view of their peers. Figure 3 shows the evolution of the average RReps of agents A when they are active and inactive. Although the average RRep of agents A declines in both cases after agents B change their behaviors at time 50, it can be seen that if agents A stay active exchanging reputation information with other peers, their average RRep picks up after some time; otherwise, their average RRep keeps dropping.

Defense against Dishonest Recommendation

The third experiment aims to show the robustness of our reputation mechanism against dishonest recommendations. It shows our mechanism's capability to incentivize honest recommendation. The experiment set includes 500 rounds (i.e., nRound = 500). At round 100, agents B become trustworthy in service recommendation. Similar to the above experiment set, we benchmark agents B 's average RRep. It can be seen from Fig. 4 that agents B have established good service recommendation reputation by round 300. Similarly, suppose at round 100, agents A become deceitful in service recommendation (other agents stay unchanged). Figure 4 shows that the average RRep of agents A falls below 0 by round 250. This proves the dynamics of reputation in our model: reputable recommenders' RReps tear down if they recommend falsely and vice versa.

Related Work

Marsh [START_REF] Marsh | Formalising Trust as a Computational Concept[END_REF] is among the first to present a formal trust model, incorporating properties of trust from psychology and sociology. It is well-founded yet complex model; it does not model reputation in the trust model. Mui, et al, [START_REF] Mui | Notions of reputation in multi-agents systems: A review[END_REF] review the existing work on reputation across diverse disciplines and give a typology of reputation, classified by the source of reputation. Our reputation model incorporates two types of reputation: interaction derived reputation and propagated reputation.

Many reputation systems do not differentiate the reputation of service provision and recommendation [START_REF] Zacharia | Trust management through reputation mechanisms[END_REF][START_REF] Xiong | Building trust in decentralized peer-to-peer electronic communities[END_REF][START_REF] Buchegger | Performance analysis of the CONFIDANT protocol[END_REF], or assume the truthfulness of recommendations [START_REF] Michiardi | CORE: a collaborative reputation mechanism to enforce node cooperation in mobile ad hoc networks[END_REF]. Some systems allow only positive recommendations [START_REF] Michiardi | CORE: a collaborative reputation mechanism to enforce node cooperation in mobile ad hoc networks[END_REF] or only negative recommendations [START_REF] Buchegger | Performance analysis of the CONFIDANT protocol[END_REF].

Abdul-Rahman and Hailes [START_REF] Abdul-Rahman | Supporting trust in virtual communities[END_REF] present a trust model, incorporating direct trust based on interaction experiences and recommender trust, which is similar to our recommendation reputation. False recommendation are dealt by recording the difference between the recommended value and the experienced value. The difference is then applied to obtain a "true" value. The result is, however, uncertain when the difference is not fixed but varied. Additionally, their work does not provide incentives to give recommendations or punishment for those giving false information.

Pretty Good Privacy (PGP) [START_REF] Zimmermann | The Official PGP User's Guide[END_REF] proposes a Web of Trust decentralized authentication scheme, by associating a public key (i.e., a recommender) with its trustworthiness of recommending name-public key binding. Agents can validate an unknown name-public key binding, or peers' credentials [START_REF] Keoh | Trust and the establishment of ad-hoc communitie[END_REF], through aggregate trust of recommendation (e.g., if a binding is recommended by a completely trusted key, it is considered valid). However, the degree of trustworthiness is static and assigned by users subjectively. Thus, it does not apply to dynamic scenarios. Reputation in our work evolves with behavior and time.

Jurca and Faltings [START_REF] Jurca | An incentive compatible reputation mechanism[END_REF] propose an incentive-compatible reputation system by introducing special broker agents named R-agents, which sell reputation information to and buy reputation information from agents. The payoff for an agent selling reputation information to an R-agent depends on whether its provided information coincides with the future reports on the same agent. The effectiveness of the proposed mechanism lies greatly on the integrity of R-agents, which assumely always exist in the system. In addition, collusion is not considered. Our mechanism defends against both collusion and defame attack by associating a reputation with each agent's recommendation behavior. Dishonest recommenders suffer low recommendation reputation, and thus their recommendations are either excluded or considered very trivial (i.e., assigned a small weight).

Conclusion and Future Work

In this paper, we have presented an enhanced reputation mechanism for mobile ad hoc networks by modeling reputation with two important dimensions, time and context, and incorporating reputation formation, evolution and propagation. Our scheme is distributed, effective and storage-saving without reliance on any trustworthy party or centralized storage.

Besides looking into incentive counterpart in sociology and psychology, our future work also includes a more formal analysis of context. As discussed, context is a multiple-facet notion, and can depend on many factors, whether subjective or objective.

We notice the problem of scalability issue with our approach. Although our mechanism does take care of the storage problem, it may still overload nodes given large distributed networks of tens of thousands of terminals. An intuitive approach is to incorporate a caching scheme with some replacement algorithm. However, discarding reputation information can be costly and requires careful tradeoff consideration.

Like most reputation systems, another unaddressed issue is changing of identities. Most online reputation systems protect privacy and each agent's identity is normally a pseudonym. It causes problems because pseudonym can be changed easily [START_REF] Zacharia | Trust management through reputation mechanisms[END_REF][START_REF] Mui | A computational model of trust and reputation[END_REF]. When a user ends up having a reputation lower than that of a new comer, the user is tempted to discard her initial identity and start from the beginning. This suggests the necessity of special treatments of new users. We plan to incorporate defense against this kind of attack in our future work.

Fig. 1 .

 1 Fig. 1. A service ontology tree

Fig. 2 .

 2 Fig. 2. Mistakes with and without RRep

Fig. 3 .

 3 Fig. 3. Changes of RRep with active and inactive without exchange

Fig. 4 .

 4 Fig. 4. Changes of RRep with higher and lower trustworthiness of RRep

Table 1 .

 1 Notations used in the model Label Value Range Meaning SRepa(o) t [-1.. + 1] service reputation of agent o held by agent a at time t RRepa(o) t [-1.. + 1] recommendation reputation of agent o held by agent a at time t SExpa(o) t [-1.. + 1] Reputation of o derived from a's interaction experiences with o Reca(o)

			SRepa(o)
	ρe,ρc	[0..1]	Fading factor, representing agent's reliance on recent be-
			haviors or related contexts

t [-1.. + 1] Recommendation made by agent a regarding agent o's reputation at time t. For honest agent a, Reca(o) =

Similar to the notion of resource in resource discovery

As explained later, observation here refers to indirect observation through peers' recommendations.

As pointed out by[START_REF] Marsh | Formalising Trust as a Computational Concept[END_REF][START_REF] Mui | A computational model of trust and reputation[END_REF] and discussed at the end of this paper, this assignment does not differentiate new comers from agents whose 0 reputation value results from previous behaviors.

For simplicity, we don't discuss context-dependent recommendation reputation here.

http://www.w3.org/2001/sw/

http://www.daml.org/services/

If the provided service does not meet functionality requirement, it is considered completely unsatisfactory.

Normalization is necessary here because ri/bi does not fall into [-1, 1], one normalization way is to define a perfect value (i.e., 1), e.g., five times the requested value. All values higher than perfect is considered perfect.

[START_REF] Gambetta | Can we trust trust? In: Trust, Making and Breaking Cooperative Relations[END_REF] ⊗ represents XOR function, i.e., x ⊗ y = 0 if x equals y, and 1

otherwise.[START_REF] Mui | A computational model of trust and reputation[END_REF] For example, weakest value is mapped to 1, the second weakest to 2, and so on.

The length of period depends on the agent's recent interactions. For example, if the agent meets strangers frequently in the recent period, it implies that it has to rely more on recommendations from peers. The need for reputation information from peers becomes stronger and the length is decreased accordingly.

In the initial phase, agents have no information of each other. Thus we only consider the last 50 rounds when each agent has built up a knowledge base for reputation evaluation.