High Stakes Behavior with Low Payoffs: Inducing Preferences with Holt-Laury Gambles

A continuing goal of experiments is to understand risky decisions when the decisions are important. Often a decision’s importance is related to the magnitude of the associated monetary stake. Khaneman and Tversky (1979) argue that risky decisions in high stakes environments can be informed using questionnaires with hypothetical choices (since subjects have no incentive to answer questions falsely.) However, results reported by Holt and Laury (2002, henceforth HL), as well as replications by Harrison (2005) suggest that decisions in “high” monetary payoff environments are not well-predicted by questionnaire responses. Thus, a potential implication of the HL results is that studying decisions in high stakes environments requires using high stakes. Here we describe and implement a procedure for studying high-stakes behavior in a low-stakes environment. We use the binary-lottery reward technique (introduced by Berg, et al (1986)) to induce preferences in a way that is consistent with the decisions reported by HL under a variety of stake sizes. The resulting decisions, all of which were made in a low-stakes environment, reflect surprisingly well the noisy choice behavior reported by HL’s subjects even in their highstakes environment. This finding is important because inducing preferences evidently requires substantially less cost than paying people to participate in extremely high-stakes games.

Endogenous Group Formation Via Unproductive Costs

Sacrifice is widely believed to enhance cooperation in churches, communes, gangs, clans, military units, and many other groups. We find that sacrifice can also work in the lab, apart from special ideologies, identities, or interactions. Our subjects play a modified VCM game—one in which they can voluntarily join groups that provide reduced rates of return on private investment. This leads to both endogenous sorting (because free-riders tend to reject the reduced-rate option) and substitution (because reduced private productivity favours increased club involvement). Seemingly unproductive costs thus serve to screen out free-riders, attract conditional cooperators, boost club production, and increase member welfare. The sacrifice mechanism is simple and particularly useful where monitoring difficulties impede punishment, exclusion, fees, and other more standard solutions

High Stakes Behavior with Low Payoffs: Inducing preferences with Holt-Laury gambles

A continuing goal of experiments is to understand risky decisions when the decisions are important. Often a decision’s importance is related to the magnitude of the associated monetary stake. Khaneman and Tversky (1979) argue that risky decisions in high stakes environments can be informed using questionnaires with hypothetical choices (since subjects have no incentive to answer questions falsely.) However, results reported by Holt and Laury (2002, henceforth HL), as well as replications by Harrison (2005) suggest that decisions in “high” monetary payoff environments are not well-predicted by questionnaire responses. Thus, a potential implication of the HL results is that studying decisions in high stakes environments requires using high stakes. Here we describe and implement a procedure for studying high-stakes behavior in a low-stakes environment. We use the binary-lottery reward technique (introduced by Berg, et al (1986)) to induce preferences in a way that is consistent with the decisions reported by HL under a variety of stake sizes. The resulting decisions, all of which were made in a low-stakes environment, reflect surprisingly well the noisy choice behavior reported by HL’s subjects even in their highstakes environment. This finding is important because inducing preferences evidently requires substantially less cost than paying people to participate in extremely high-stakes games

Tracking Cyber Adversaries with Adaptive Indicators of Compromise

A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques, and procedures (TTPs), which will also change the data generated. If the IOCs are not kept up-to-date with the adversary's new TTPs, the adversary will no longer be detected once all of the IOCs become invalid. Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular expressions (regexes), up-to-date with a dynamic adversary. Our framework solves the TTK problem in an automated, cyclic fashion to bracket a previously discovered adversary. This tracking is accomplished through a data-driven approach of self-adapting a given model based on its own detection capabilities. In our initial experiments, we found that the true positive rate (TPR) of the adaptive solution degrades much less significantly over time than the naive solution, suggesting that self-updating the model allows the continued detection of positives (i.e., adversaries). The cost for this performance is in the false positive rate (FPR), which increases over time for the adaptive solution, but remains constant for the naive solution. However, the difference in overall detection performance, as measured by the area under the curve (AUC), between the two methods is negligible. This result suggests that self-updating the model over time should be done in practice to continue to detect known, evolving adversaries.Comment: This was presented at the 4th Annual Conf. on Computational Science & Computational Intelligence (CSCI'17) held Dec 14-16, 2017 in Las Vegas, Nevada, US

Neurogenesis Deep Learning

Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing - data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information in an already trained network. As a result, methods for continuous learning are potentially highly impactful in enabling the application of deep networks to dynamic data sets. Here, inspired by the process of adult neurogenesis in the hippocampus, we explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations. Our results on the MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes lower and upper case letters and digits, demonstrate that neurogenesis is well suited for addressing the stability-plasticity dilemma that has long challenged adaptive machine learning algorithms.Comment: 8 pages, 8 figures, Accepted to 2017 International Joint Conference on Neural Networks (IJCNN 2017

Multi-level optical signal generation using a segmented-electrode InP IQ-MZM with integrated CMOS binary drivers

We present a segmented-electrode InP IQ-MZM, capable of multi-level optical signal generation (5-bit per I/Q arm) by employing direct digital drive from integrated, low-power (1W) CMOS binary drivers. Programmable, multi-level operation is demonstrated experimentally on one MZM of the device

Tratamiento de la hipertensión arterial en pacientes hipertensos crónicos con accidente cerebrovascular : Hospital Evita Pueblo. Berazategui. 2015

Objetivo: describir los antecedentes de HTA y su tratamiento farmacológico en pacientes con enfermedad cardiovascular establecida (ACV). Comparar con la bibliografía.Facultad de Ciencias Médica

Tratamiento de la hipertensión arterial en pacientes hipertensos crónicos con accidente cerebrovascular : Hospital Evita Pueblo. Berazategui. 2015

Objetivo: describir los antecedentes de HTA y su tratamiento farmacológico en pacientes con enfermedad cardiovascular establecida (ACV). Comparar con la bibliografía.Facultad de Ciencias Médica