Competing With Strategies

We study the problem of online learning with a notion of regret defined with respect to a set of strategies. We develop tools for analyzing the minimax rates and for deriving regret-minimization algorithms in this scenario. While the standard methods for minimizing the usual notion of regret fail, through our analysis we demonstrate existence of regret-minimization methods that compete with such sets of strategies as: autoregressive algorithms, strategies based on statistical models, regularized least squares, and follow the regularized leader strategies. In several cases we also derive efficient learning algorithms

Competing with stationary prediction strategies

In this paper we introduce the class of stationary prediction strategies and construct a prediction algorithm that asymptotically performs as well as the best continuous stationary strategy. We make mild compactness assumptions but no stochastic assumptions about the environment. In particular, no assumption of stationarity is made about the environment, and the stationarity of the considered strategies only means that they do not depend explicitly on time; we argue that it is natural to consider only stationary strategies even for highly non-stationary environments.Comment: 20 page

Competing with Markov prediction strategies

Assuming that the loss function is convex in the prediction, we construct a prediction strategy universal for the class of Markov prediction strategies, not necessarily continuous. Allowing randomization, we remove the requirement of convexity.Comment: 11 page

Predictable Sequences and Competing with Strategies

First, we study online learning with an extended notion of regret, which is defined with respect to a set of strategies. We develop tools for analyzing the minimax rates and deriving efficient learning algorithms in this scenario. While the standard methods for minimizing the usual notion of regret fail, through our analysis we demonstrate the existence of regret-minimization methods that compete with such sets of strategies as: autoregressive algorithms, strategies based on statistical models, regularized least squares, and follow-the-regularized-leader strategies. In several cases, we also derive efficient learning algorithms. Then we study how online linear optimization competes with strategies while benefiting from the predictable sequence. We analyze the minimax value of the online linear optimization problem and develop algorithms that take advantage of the predictable sequence and that guarantee performance compared to fixed actions. Later, we extend the story to a model selection problem on multiple predictable sequences. At the end, we re-analyze the problem from the perspective of dynamic regret. Last, we study the relationship between Approximate Entropy and Shannon Entropy, and propose the adaptive Shannon Entropy approximation methods (e.g., Lempel-Ziv sliding window method) as an alternative approach to quantify the regularity of data. The new approach has the advantage of adaptively choosing the order of regularity

How open is open enough?: Melding proprietary and open source platform strategies

Computer platforms provide an integrated architecture of hardware and software standards as a basis for developing complementary assets. The most successful platforms were owned by proprietary sponsors that controlled platform evolution and appropriated associated rewards. Responding to the Internet and open source systems, three traditional vendors of proprietary platforms experimented with hybrid strategies which attempted to combine the advantages of open source software while retaining control and differentiation. Such hybrid standards strategies reflect the competing imperatives for adoption and appropriability, and suggest the conditions under which such strategies may be preferable to either the purely open or purely proprietary alternatives

EU-Russia relations in the context of the eastern neighbourhood

This report briefly examines EU-Russia relations in the context of the eastern neighbourhood. It contends that both the EU and Russia’s ambitions for the eastern region have evolved into two competing region-building projects underpinned by differing strategies, norms, instruments, and actors. Although projecting competing rationalities, the two projects, until recently, had peacefully co-existed, working around conflicting issues of political norms and economic convergence, which were not necessarily seen as insurmountable for furthering regional cooperation. Their subsequent politicisation and securitisation, as a consequence of events in Ukraine, have rendered regional partnership currently incompatible, revealing a profound lack of understanding the region by both the EU and Russia; and the EU under-exploited capacity to work co-jointly with the Eurasian Union (and Russia) vis-a-vis the region. This report contends that the EU must make an effort to acknowledge and engage with the above actors in the region, in order to develop cooperative strategies, based on shared interests, international norms and compatible instruments for the advancement of economic and political convergence

Lima Strategi Untuk Bersaing Melalui Layanan Di Rumah Makan Pondok Milenium

Researching was conducted with the aim to formulate the right strategy in marketing services and products to use at restaurant with five strategies for competing through service.Them main problem is needed in this metode is a decrease in the number of consumers who visit for a period months.The study uses a case study type of research with a sample of 100 people consiting of consumers who use the service and products of the restaurant.The result of this study indicate that the perception of consumers agree and satisfied with the service five strategies to compete with services used by the restaurant that given statisfaction to the consumer

Growth or Reproduction: Emergence of an Evolutionary Optimal Strategy

Modern ecology has re-emphasized the need for a quantitative understanding of the original 'survival of the fittest theme' based on analyzis of the intricate trade-offs between competing evolutionary strategies that characterize the evolution of life. This is key to the understanding of species coexistence and ecosystem diversity under the omnipresent constraint of limited resources. In this work we propose an agent based model replicating a community of interacting individuals, e.g. plants in a forest, where all are competing for the same finite amount of resources and each competitor is characterized by a specific growth-reproduction strategy. We show that such an evolution dynamics drives the system towards a stationary state characterized by an emergent optimal strategy, which in turn depends on the amount of available resources the ecosystem can rely on. We find that the share of resources used by individuals is power-law distributed with an exponent directly related to the optimal strategy. The model can be further generalized to devise optimal strategies in social and economical interacting systems dynamics.Comment: 10 pages, 5 figure