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Azaria A.,Carnegie Mellon University | Richardson A.,Lev Academic Center | Kraus S.,Bar - Ilan University
CSCW 2015 - Proceedings of the 2015 ACM International Conference on Computer-Supported Cooperative Work and Social Computing | Year: 2015

Extensive use of computerized forums and chat-rooms provides a modern venue for deception. We propose introducing an agent to assist in detecting and incriminating a deceptive participant. We designed a game, where deception in a text based discussion environment occurs. In this game several participants attempt to collectively detect a deceptive member. We compose an automated agent which participates in this game as a regular player. The goal of the agent is to detect the deceptive participant and alert other members, without raising suspicion itself. We use machine learning on the data collected from human players to design this agent. Extensive evaluation of our agent shows that it succeeds in raising the players collective success rate in catching the deceptive player. © 2015 ACM. Source


Nowik I.,Lev Academic Center
International Game Theory Review | Year: 2016

The purpose of this work is to offer for each player and any Nash equilibrium (NE), a measure for the potential risk in deviating from the NE strategy in any two person matrix game. We present two approaches regarding the nature of deviations: Strategic and Accidental. Accordingly, we define two models: S-model and T-model. The S-model defines a new game in which players deviate in the least dangerous direction. The risk defined in the T-model can serve as a refinement for the notion of “trembling hand perfect equilibrium” introduced by R. Selten. The risk measures enable testing and evaluating predictions on the behavior of players. For example: do players deviate more from a NE that is less risky? This may be relevant to the design of experiments. We present an Integer programming problem that computes the risk for any given player and NE. In the special case of zero-sum games with a unique strictly mixed NE, we prove that the risks of the players always coincide, even if the game is far from symmetry. This result holds for any norm we use for the size of deviations. We compare our risk measures to the risk measure defined by Harsanyi and Selten which is based on criteria of stability rather than on potential damage. We show that the measures may contradict. © 2016 World Scientific Publishing Company Source


Heifetz E.M.,Lev Academic Center | Soller M.,Hebrew University of Jerusalem
BMC Genetics | Year: 2015

Background: High-resolution mapping of the loci (QTN) responsible for genetic variation in quantitative traits is essential for positional cloning of candidate genes, and for effective marker assisted selection. The confidence interval (QTL) flanking the point estimate of QTN-location is proportional to the number of individuals in the mapping population carrying chromosomes recombinant in the given interval. Consequently, many designs for high resolution QTN mapping are based on increasing the proportion of recombinants in the mapping population. Results: In the absence of residual polygenic variation, population sizes required for achieving given mapping resolution by the TRP-F2 design relative to a standard F2 design ranged from 0.289 for a QTN with standardized allele substitution effect = 0.2, mapped to an initial QTL of 0.2 Morgan to 0.041 for equivalent QTN mapped to an initial QTL of 0.02 M. In the presence of residual polygenic variation, the relative effectiveness of the TRP design ranges from 1.068 to 0.151 for the same initial QTL intervals and QTN effect. Thus even in the presence of polygenic variation, the TRP can still provide major savings. Simulation showed that mapping by TRP should be based on 30-50 markers spanning the initial interval; and on at least 50 or more G2 families representing this number of recombination points,. Conclusions: The TRP design can be an effective procedure for achieving high and ultra-high mapping resolution of a target QTN previously mapped to a known confidence interval (QTL). © 2015 Heifetz and Soller. Source


Karsenty A.,Lev Academic Center | Chelly A.,Bar - Ilan University
Active and Passive Electronic Components | Year: 2015

Nanoscale Gate-Recessed Channel (GRC) Fully Depleted- (FD-) SOI MOSFET device with a silicon channel thickness (tSi) as low as 2.2 nm was first tested at room temperature for functionality check and then tested at low temperature (77 K) for I-V characterizations. In spite of its FD-SOI nanoscale thickness and long channel feature, the device has surprisingly exhibited a Drain-Induced Barrier Lowering (DIBL) effect at RT. However, this effect was suppressed at 77 K. If the apparition of such anomalous effect can be explained by a parasitic short channel transistor located at the edges of the channel, its suppression is explained by the decrease of the potential barrier between the drain and the channel when lowering the temperature. © 2015 Avi Karsenty and Avraham Chelly. Source


Nowik I.,Lev Academic Center
Proceedings - 2nd International Symposium on Stochastic Models in Reliability Engineering, Life Science, and Operations Management, SMRLO 2016 | Year: 2016

The purpose of this work is to offer for any zero-sum game with a unique strictly mixed Nash equilibrium, a measure for the risk when deviating from the Nash equilibrium. We present two approaches regarding the nature of deviations, strategic and accidental. Accordingly, we define two models, S-model and T-model. In each model we define risk measures for the row-player (PI) and the column player (PII). The S-model defines a new game in which players deviate in the least dangerous direction. The risk defined in the T-model can serve as a refinement for the notion of «trembling hand perfect equilibrium» introduced by Selten. We prove that a player can never deviate without any risk. We develop analytical methods for calculating the risks and the strategies that support it for each player in each of the S-and T-models. The risk measures defined here enables testing and evaluating predictions on the behavior of players. For example: Do players deviate more in a game with lower risks than in a game with higher risk? © 2016 IEEE. Source

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