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First, in the corruption experiment, individuals in the high-monitoring group commit fewer corrupt acts than those in the low-monitoring groups.
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Sugrobov and Kolesnikov did, however, bring a new style of investigation to the fight against corruption: the "operational experiment," a sting in which, acting on the instructions of GUEBiPK, a businessman or a state official working undercover tries to pass a bribe to a higher-level bureaucrat.
The resulted AAMs and HAMs are demonstrated to have excellent tolerance and robustness to a variety of input noises and corruption via comparative experiments on retrieval and association of 2D color images.
This paper reports on a controlled field experiment on corruption designed to address two important issues: the experimenter's scrutiny and the unobservability of corruption.
The first paper that analyzed corruption in a laboratory experiment was Abbink et al. (2002).
Using international university students in a bribery game, Barr and Serra (2010) find that corruption in the laboratory experiment is related to corruption in their country of origin.
For our experiment on corruption, we designed a coordination game to model the influence of risk attitudes, beliefs, and information on behavioral choices and determined the equilibria.
Other experiments related to corruption and cooperation include tax compliance experiments for an example see Kogler et al. (2013).
Table 2 Types of synthetic corruptions used in experiments Experiment 1: segmentation Experiment 2: reconstruction Additive white Guassian noise (AWGN): we use a standard AWGN algorithm [[22]] Transient corruption: We use the following types of transient corruptions.
The laboratory experiments on corruption and cooperation discussed above have found robust results characterizing the relevant variables, but the relation between corruption and cooperation has not yet been clarified.
Signals, corruptions and setup: For the signal generation, we use the same parameters as in the last experiment, and for the corruption, we use the random erasure masks of 'type22' with p 1=q 1=0.5/0.7 and p 2=q 2=0.7/0.9 corresponding to 36 and 64% of corruption.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com