Learning while voting: determinants of collective experimentation

This paper analyzes collective decision making when individual preferences evolve through learning. Votes are affected by their anticipated effect on future preferences. The analysis is conducted in a two-arm bandit model with a safe alternative and a risky alternative whose payoff distribution, or “type”, varies across individuals and may be learned through experimentation. Society is shown to experiment less than any of its members would if he could dictate future decisions, and to be systematically biased against experimentation compared to the utilitarian optimum. Control sharing can even result in negative value of experimentation: society may shun a risky alternative even its expected payoff is higher than the safe one’s. Commitment to a fixed alternative can only increase efficiency if aggregate uncertainty is small enough. Even when types are independent, a positive news shock for anyone raises everyone’s incentive to experiment. Ex ante preference correlation or heterogeneity reduces these inefficiencies.

Optimal Electoral Timing: Exercise Wisely and You May Live Longer

In many democratic countries, the timing of elections is flexible. We explore this potentially valuable option using insights from option pricing in finance. The paper offers three main contributions on this problem. First, we derive a rationally-based mean-reverting political support process for the parties, assuming that politically heterogeneous voters continuously learn over time about evolving party fortunes. We solve for the long-run density for this process and derive the polling process from it by adding polling noise. Second, we explore optimal timing using the political support process. The incumbent sees its poll support, and must call an election within five years of the last election to maximize its expected total time in office. This resembles the optimal exercise rule for an American financial option. This option is recursive, and the waiting and stopping values subtly interact. We prove the existence of the optimal exercise rule in this setting, and show that the expected longevity is a convex-thenconcave function of the political support. Our model is tractable enough that we can analytically derive how the exercise rule responds to parametric shifts. We calibrate our model to the Labour-Tory rivalry in the U.K., with polling data from 1943-2005 and the 16 elections after 1945. Excluding three elections essentially forced by weak governments, our maximizing story quite well explains when the elections were called, and beats simple linear regressions. We also measure the value of election options, finding that over the long run they should more than double the expected time in power of a fixed term electoral cycle.American option, European option, Brownian motion, Electoral timing

COMET: A Recipe for Learning and Using Large Ensembles on Massive Data

COMET is a single-pass MapReduce algorithm for learning on large-scale data. It builds multiple random forest ensembles on distributed blocks of data and merges them into a mega-ensemble. This approach is appropriate when learning from massive-scale data that is too large to fit on a single machine. To get the best accuracy, IVoting should be used instead of bagging to generate the training subset for each decision tree in the random forest. Experiments with two large datasets (5GB and 50GB compressed) show that COMET compares favorably (in both accuracy and training time) to learning on a subsample of data using a serial algorithm. Finally, we propose a new Gaussian approach for lazy ensemble evaluation which dynamically decides how many ensemble members to evaluate per data point; this can reduce evaluation cost by 100X or more

Law and post-communist countries: case of Albania

Communist regimes in general and especially the one in Albania destroyed almost every aspect of political, social, cultural and economic life, including the notion of pluralism and intellectual elite of the country. In Albania, the transition into democracy in 90’ was done through extrication which means that the authoritarian government was weakened, but not as thoroughly as in a transition by defeat. As a consequence, the former Communist elite was able to negotiate crucial features of the transition and was very quickly transformed into the new pluralist political class. This position enabled the communist elite to be rehabilitated and together with the new emerged communist elite to remain a strong influential actor in new emerged democracy and de facto to run in continuance the country. The purpose of the new emerged communist elite to maintain control was favored inter alia by the absence of a new strong intellectual elite and was done merely by sharing the power among its members divided into different political parties and also by using the ‘pluralist’ law as a tool for social control over new emerging intellectual elites. The use of law as a tool for social control by the political class has severely damaged people's understanding and expectations on the law, its relations with the state as well as international community. Indeed, such experience of the use of law by the political class for its own narrow interests, has made people lose confidence in law and state as well as has severely weakened the law enforcement in the country. To conclude, the overall purpose of this paper would be the analysis of law in general and its understandings and development in a post-communist society such as Albania from different points of view

For a learnable mathematics in the digital cultures

I begin with some general remarks concerning the co-evolution of representational forms and mathematical meanings. I then discuss the changed roles of mathematics and novel representations that emerge from the ubiquity of computational models, and briefly consider the implications for learning mathematics. I contend that a central component of knowledge required in modern societies involves the development of a meta-epistemological stance – i.e. developing a sense of mechanism for the models that underpin social and professional discourses. I illustrate this point in relation to recent research in which I am investigating the mathematical epistemology of engineering practice. Finally, I map out one implication for the design of future mathematical learning environments with reference to some data from the "Playground Project"

Prerendered User Interfaces for Higher-Assurance Electronic Voting

We propose an electronic voting machine architecture in which the voting user interface is prerendered and published before election day. The prerendered user interface is a verifiable artifact—an electronic sample ballot—enabling public participation in the review, verification, usability testing, and accessibility testing of the ballot. Preparing the user interface outside of the voting machine dramatically reduces the amount and difficulty of software verification required to assure the correctness of the election result. We present a design for a high-assurance touchscreen voting machine that supports a wide range of user interface styles and demonstrate its feasibility by implementing it in less than 300 lines of Python code

Gossip Learning with Linear Models on Fully Distributed Data

Machine learning over fully distributed data poses an important problem in peer-to-peer (P2P) applications. In this model we have one data record at each network node, but without the possibility to move raw data due to privacy considerations. For example, user profiles, ratings, history, or sensor readings can represent this case. This problem is difficult, because there is no possibility to learn local models, the system model offers almost no guarantees for reliability, yet the communication cost needs to be kept low. Here we propose gossip learning, a generic approach that is based on multiple models taking random walks over the network in parallel, while applying an online learning algorithm to improve themselves, and getting combined via ensemble learning methods. We present an instantiation of this approach for the case of classification with linear models. Our main contribution is an ensemble learning method which---through the continuous combination of the models in the network---implements a virtual weighted voting mechanism over an exponential number of models at practically no extra cost as compared to independent random walks. We prove the convergence of the method theoretically, and perform extensive experiments on benchmark datasets. Our experimental analysis demonstrates the performance and robustness of the proposed approach.Comment: The paper was published in the journal Concurrency and Computation: Practice and Experience http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291532-0634 (DOI: http://dx.doi.org/10.1002/cpe.2858). The modifications are based on the suggestions from the reviewer

The Development of a Reduced Glycemic Load/High Fiber Pasta Using Pulses

The use of beans in the human diet provides an excellent source of dietary fiber and has potential for lowering glycemic load. Prepared meals with high levels of dietary fiber and low glycemic loads were found to be rare in a market survey of nine stores with various price points in the greater Baton Rouge area. The majority of the prepared meals found fell into low or medium fiber categories with medium to high glycemic loads. This indicates a need to increase the fiber level and decrease the glycemic load in popular foods. The purpose of this research is to accomplish these changes in prepared meals by substituting a portion of the standard pasta flour with bean flour. Various mixtures of pinto bean, navy bean, black bean, enriched semolina, and “00” flours (a high-gluten red spring wheat flour) were tested using a standard Rapid Visco Analysis method and the visco-elastic properties were compared with the control flour. The addition of navy bean four to the control flour was found to produce a composite flour with a similar texture at 25%, 30%, and 50% substitution levels. A calculated proximate analysis was performed on three ravioli produced: a control, a 50%, and a 75% navy bean ravioli. A 14% DV and a 21% DV increase in dietary fiber were predicted for the 50% and 75% navy bean ravioli, respectively. A seven and a ten gram decrease in glycemic load were predicted for the 50% and the 75% navy bean ravioli. The three ravioli types were also subjected to a sensory study with 103 participants. It was found that the color, texture, aroma, appearance, and liking preferences were not significantly changed by the substitution of navy bean flour at a 50% substitution level (α = 0.05). These characteristics of commercially available frozen pasta meals were also measured with a blind consumer survey of consumers ages 65 and older. The predominant unsatisfactory characteristics found were texture and color. In comparison, the texture and color were not significantly altered by the 50% substitution of navy bean flour in the ravioli sensory study

Spartan Daily, February 23, 1948

Volume 36, Issue 89https://scholarworks.sjsu.edu/spartandaily/11049/thumbnail.jp