Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAnalyzing Incentives for Protocol Compliance in Complex Domains: A Case Study of Introduction-Based Routing
216
0
0.0
(
0
)
Added by
Michael Wellman
Publication date
2013
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Formal analyses of incentives for compliance with network protocols often appeal to game-theoretic models and concepts. Applications of game-theoretic analysis to network security have generally been limited to highly stylized models, where simplified environments enable tractable study of key strategic variables. We propose a simulation-based approach to game-theoretic analysis of protocol compliance, for scenarios with large populations of agents and large policy spaces. We define a general procedure for systematically exploring a structured policy space, directed expressly to resolve the qualitative classification of equilibrium behavior as compliant or non-compliant. The techniques are illustrated and exercised through an extensive case study analyzing compliance incentives for introduction-based routing. We find that the benefits of complying with the protocol are particularly strong for nodes subject to attack, and the overall compliance level achieved in equilibrium, while not universal, is sufficient to support the desired security goals of the protocol.
rate research
Read More
We propose the first Reversible Coherence Protocol (RCP), a new protocol designed from ground up that enables invisible speculative load. RCP takes a bold approach by including the speculative loads and merge/purge operation in the interface between processor and cache coherence, and allowing them to participate in the coherence protocol. It means, speculative load, ordinary load/store, and merge/purge can all affect the state of a given cache line. RCP is the first coherence protocol that enables the commit and squash of the speculative load among distributed cache components in a general memory hierarchy. RCP incurs an average slowdown of (3.0%,8.3%,7.4%) on (SPEC2006,SPEC2017,PARSEC), which is lower compared to (26.5%,12%,18.3%) in InvisiSpec and (3.2%,9.4%,24.2%) in CleanupSpec. The coherence traffic overhead is on average 46%, compared to 40% and 27% of InvisiSpec and CleanupSpec, respectively. Even with higher traffic overhead (~46%), the performance overhead of RCP is lower than InvisiSpec and comparable to CleanupSpec. It reveals a key advantage of RCP: the coherence actions triggered by the merge and purge operations are not in the critical path of the execution and can be performed in the cache hierarchy concurrently with processor execution
Vehicle-to-everything (V2X) communication in the vehicular ad hoc network (VANET), an infrastructure-free mechanism, has emerged as a crucial component in the advanced Intelligent Transport System (ITS) for special information transmission and inter-vehicular communications. One of the main research challenges in VANET is the design and implementation of network routing protocols which manage to trigger V2X communication with the reliable end-to-end connectivity and efficient packet transmission. The organically changing nature of road transport vehicles poses a significant threat to VANET with respect to the accuracy and reliability of packet delivery. Therefore, a position-based routing protocol tends to be the predominant method in VANET as they overcome rapid changes in vehicle movements effectively. However, existing routing protocols have some limitations such as (i) inaccurate in high dynamic network topology, (ii) defective link-state estimation (iii) poor movement prediction in heterogeneous road layouts. In this paper, a target-driven and mobility prediction (TDMP) based routing protocol is therefore developed for high-speed mobility and dynamic topology of vehicles, fluctuant traffic flow and diverse road layouts in VANET. The primary idea in TDMP is that the destination target of a driver is included in the mobility prediction to assist the implementation of the routing protocol. Compared to existing geographic routing protocols which mainly greedily forward the packet to the next-hop based on its current position and partial road layout, TDMP is developed to enhance the packet transmission with the consideration of the estimation of inter-vehicles link status, and the prediction of vehicle positions dynamically in fluctuant mobility and global road layout.
Social dilemmas exist in various fields and give rise to the so-called free-riding problem, leading to collective fiascos. The difficulty of tracking individual behaviors makes egoistic incentives in large-scale systems a challenging task. However, the state-of-the-art mechanisms are either individual-based or state-dependent, resulting in low efficiency in large-scale networks. In this paper, we propose an egoistic incentive mechanism from a connected (network) perspective rather than an isolated (individual) perspective by taking advantage of the social nature of people. We make use of a zero-determinant (ZD) strategy for rewarding cooperation and sanctioning defection. After proving cooperation is the dominant strategy for ZD players, we optimize their deployment to facilitate cooperation over the whole system. To further speed up cooperation, we derive a ZD alliance strategy for sequential multiple-player repeated games to empower ZD players with higher controllable leverage, which undoubtedly enriches the theoretical system of ZD strategies and broadens their application domain. Our approach is stateless and stable, which contributes to its scalability. Extensive simulations based on a real world trace data as well as synthetic data demonstrate the effectiveness of our proposed egoistic incentive approach under different networking scenarios.
We consider settings in which we wish to incentivize myopic agents (such as Airbnb landlords, who may emphasize short-term profits and property safety) to treat arriving clients fairly, in order to prevent overall discrimination against individuals or groups. We model such settings in both classical and contextual bandit models in which the myopic agents maximize rewards according to current empirical averages, but are also amenable to exogenous payments that may cause them to alter their choices. Our notion of fairness asks that more qualified individuals are never (probabilistically) preferred over less qualified ones [Joseph et al]. We investigate whether it is possible to design inexpensive {subsidy} or payment schemes for a principal to motivate myopic agents to play fairly in all or almost all rounds. When the principal has full information about the state of the myopic agents, we show it is possible to induce fair play on every round with a subsidy scheme of total cost $o(T)$ (for the classic setting with $k$ arms, $tilde{O}(sqrt{k^3T})$, and for the $d$-dimensional linear contextual setting $tilde{O}(dsqrt{k^3 T})$). If the principal has much more limited information (as might often be the case for an external regulator or watchdog), and only observes the number of rounds in which members from each of the $k$ groups were selected, but not the empirical estimates maintained by the myopic agent, the design of such a scheme becomes more complex. We show both positive and negative results in the classic and linear bandit settings by upper and lower bounding the cost of fair subsidy schemes.
Collective intelligence is the ability of a group to perform more effectively than any individual alone. Diversity among group members is a key condition for the emergence of collective intelligence, but maintaining diversity is challenging in the face of social pressure to imitate ones peers. We investigate the role incentives play in maintaining useful diversity through an evolutionary game-theoretic model of collective prediction. We show that market-based incentive systems produce herding effects, reduce information available to the group and suppress collective intelligence. In response, we propose a new incentive scheme that rewards accurate minority predictions, and show that this produces optimal diversity and collective predictive accuracy. We conclude that real-world systems should reward those who have demonstrated accuracy when majority opinion has been in error.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
