Arbitrary Policy Research Articles

Revolutionizing Freelancing: A Blockchain-Powered Paradigm for Security, Transparency, and Efficiency in the Digital Age

The Decentralized Freelancing Web Application revolutionizes freelancing by leveraging blockchain for trust and transparency. Addressing issues prevalent in centralized platforms, it eliminates high fees, ensuring fair compensation for freelancers while advocating for fair treatment, anti-fraud measures, and transparent dispute resolution. This paradigm shift fosters a dynamic and empowering global freelancing community, overcoming the pitfalls of unreliable platforms and arbitrary policies. KEYWORD’S Blockchain, Decentralized, Smart contracts, Next.js, IPFS, Centralized freelancing.

Multi-key and Multi-input Predicate Encryption (for Conjunctions) from Learning with Errors

We put forward two natural generalizations of predicate encryption (PE), dubbed multi-key and multi-input PE. More in details, our contributions are threefold.Definitions. We formalize security of multi-key PE and multi-input PE following the standard indistinguishability paradigm, and modeling security both against malicious senders (i.e., corruption of encryption keys) and malicious receivers (i.e., collusions).Constructions. We construct adaptively secure multi-key and multi-input PE supporting the conjunction of poly-many arbitrary single-input predicates, assuming the sub-exponential hardness of the learning with errors (LWE) problem.Applications. We show that multi-key and multi-input PE for expressive enough predicates suffices for interesting cryptographic applications, including non-interactive multi-party computation (NI-MPC) and matchmaking encryption (ME). In particular, plugging in our constructions of multi-key and multi-input PE, under the sub-exponential LWE assumption, we obtain the first ME supporting arbitrary policies with unbounded collusions, as well as robust (resp. non-robust) NI-MPC for so-called all-or-nothing functions satisfying a non-trivial notion of reusability and supporting a constant (resp. polynomial) number of parties. Prior to our work, both of these applications required much heavier tools such as indistinguishability obfuscation or compact functional encryption.

The Bumpy Road to Good Governance and Public Service Delivery : Analysis of the Challenges to Strategic Plans Implementation in Public Organisations in The Gambia

Scholars have long established the connections between strategic plans and service delivery and how lack of dedicated plans to clinically bypass the attendant complexities and intricacies tends to undermine the efficacy of the implementation process. Most research on the crucial antidote against bad governance, which is often attributed to arbitrary policy making, dearth of accountable leadership and uninformed decisions have been conducted in the developed climes where public service delivery are comparatively better served. Research about strategic plans in Africa, where most public institutions grope in the dark for lack of strategic torchlights to navigate the bumpy road to delivering public goods at a challenging time when citizens are becoming more demanding of governance dividends, is commensurably low. The paper, therefore, analysed the challenges to strategic plans implementation in achieving the objectives of public service organisations (PSOs) in The Gambia. Cross-sectional data about institutional challenges to adopted strategic plans were collected from relevant authorities and key informants, and descriptively analysed. The study revealed a range of challenges confronting implementation of strategic plans by PSOs in the country, namely, poor public service emoluments resulting in low staff morale and systemic corruption (RII = 3.90), political interference (RII = 3.88), inadequate facilities (RII = 3.86), lack of top management commitment (RII = 3.81), lack of political will (RII = 3.81), inadequate strategic management professionals and expertise (RII = 3.67), and insufficient budgetary allocations (RII = 3.66). Added to this, negative employee behaviour and attitude, negative and unfavourable internal and external environmental changes and conditions, and corruption and mismanagement of funds among others were the challenges that confronted PSOs during strategic plans implementation. The research validates strategic plan as a good governance tool for public service delivery in The Gambia.

Boosting On-Policy Actor-Critic With Shallow Updates in Critic.

Deep reinforcement learning (DRL) benefits from the representation power of deep neural networks (NNs), to approximate the value function and policy in the learning process. Batch reinforcement learning (BRL) benefits from stable training and data efficiency with fixed representation and enjoys solid theoretical analysis. This work proposes least-squares deep policy gradient (LSDPG), a hybrid approach that combines least-squares reinforcement learning (RL) with online DRL to achieve the best of both worlds. LSDPG leverages a shared network to share useful features between policy (actor) and value function (critic). LSDPG learns policy, value function, and representation separately. First, LSDPG views deep NNs of the critic as a linear combination of representation weighted by the weights of the last layer and performs policy evaluation with regularized least-squares temporal difference (LSTD) methods. Second, arbitrary policy gradient algorithms can be applied to improve the policy. Third, an auxiliary task is used to periodically distill the features from the critic into the representation. Unlike most DRL methods, where the critic algorithms are often used in a nonstationary situation, i.e., the policy to be evaluated is changing, the critic in LSDPG is working on a stationary case in each iteration of the critic update. We prove that, under some conditions, the critic converges to the regularized TD fixpoint of current policy, and the actor converges to the local optimal policy. The experimental results on challenging Procgen benchmark illustrate the improvement of sample efficiency of LSDPG over proximal policy optimization and phasic policy gradient (PPG).

‘We shall probably call the place Zaria, as it is such a pretty name’ – Medical discourse and town planning in Northern Nigeria, 1899–1914

Early colonial administrations faced multiple challenges in their daily work. In specific areas, administrators reached out to science as a tool to understand and support their decisions. Town planning was an issue that brought together Europeans and Africans, and therefore created common living and working spaces for them. It became an important issue immediately after the conquest of Northern Nigerian territories, especially because a growing number of Europeans started living in the region on a more permanent basis. Town planning often seemed an arbitrary policy, yet, as it is demonstrated in this paper, daily administration reached out to science as helping tool to guide and validate their goals. Segregating Europeans from Africans was a question that very soon became a hotly debated topic, that was supported or rejected by some governors. This contested question shows that colonial policy was not by default racial based, let alone racist.

Правовая и политическая реформы и изменения в Конституциях СССР и РСФСР периода перестройки (1985 – начало 1990-х гг.)

The article introduces an analysis of legal and political reforms in the USSR and the RSFSR during the Perestroika. The research objective was to describe the implementation of political and legal reforms. The analysis was based on the systematic approach, which considers independent political and legal processes in their interaction and development. The research principles included various general scientific methods and the principle of historicism, as well as comparative legal and formal legal methods. The analysis of sources and scientific literature proved that the authorities pursued a spontaneous and sometimes arbitrary policy, especially in the reform of the political system and the legal reform. While the early Perestroika was associated with the theory of the socialist rule of law, legal theorists found no special principles and norms that would distinguish it from the concepts of Western liberal and bourgeois legal thought. The fact that the government rejected the postulates of socialism caused chaotic changes in the Constitutions of the USSR and the RSFSR. When the Union center lost its positions, the initiative moved to the republican and regional levels. As a result, the constitutional reform of the late 1980s – early 1990s increased the confrontation with the allied authorities and led to domestic political and legal conflicts.

A deductible comparison of cost per-loss, cost per-payment event, loss elimination ratio under exponentially and lognormally distributed severities

The maximum amount of losses retained by the insured under deductible policy modifications is usually set as part of the terms of the policy conditions. The objective of this study are to (i) estimate mean losses of an insured risk by means of the operational behavior of density function with deductible modifications and (ii) compare the mean severities under exponentially and log-normally distributed arbitrary policy in a cost per loss and cost per payment contingencies. The results show that despite the established fact in literature that log-normal severity distribution has a thicker tail than the exponential distribution, its cost per loss payment is correspondingly lower than the corresponding values of the exponential mean loss. Computational evidence over the trend of the change in the loss eliminated in the domain for which deductible has been defined revealed that the cost per loss amount is less than the cost per payment amount in the two models. The method can be used to estimate aggregate claims as the deductible level increases for every scheme holder and such that the estimated claims could be compared with the hypothetical observed claims which can be arrived at by applying the hypothetical deductible value to the background losses.

Commentary on Glanz et al.: Longer buprenorphine treatment duration is associated with lower rates of opioid overdose, but can we address barriers to staying in treatment?

Glanz et al. examine the association between treatment duration and outcomes including all-cause mortality and overdose, demonstrating that treatment of at least 12 months is associated with lower opioid overdose rates. We now need to understand why people leave treatment after shorter periods of time, and what can be done to improve treatment retention. Opioid dependence, and particularly untreated opioid dependence, places an enormous health and economic burden on individuals and society. As opioid-related deaths in North America have continued to increase, and increase more steeply [1], the provision of evidence-based treatments such as buprenorphine has been increasingly critical. The study by Glanz et al. [2] addresses one key aspect of providing buprenorphine treatment for which there is currently limited information—optimal treatment duration. The study examines buprenorphine discontinuation with different treatment durations in relation to the critical outcomes of all-cause mortality and overdose. The study provides evidence that risk of opioid overdose may be greater following discontinuation of treatment episodes of shorter duration relative to treatment of 12 months duration or longer, with the authors concluding that their findings did not support limitations on treatment duration. It is well established that short-term treatments are ineffective for most and that longer-term treatments such as opioid agonist treatment are critical in reducing mortality [3]. This new study represents an addition to the limited evidence on how long opioid agonist treatment should continue. Clinical guidelines vary, but often propose that there should not be a hard limit on how long treatment continues. The World Health Organization guidelines state that opioid agonists treatments can be continued indefinitely, and for some patients this may be optimal [4]. For patients who may seek advice regarding ‘how long is long enough?’, providing a clear answer based on the existing evidence is currently challenging, but the work by Glanz and others helps to address this evidence gap. Questions regarding how long treatment should be continued become particularly important where access to treatment is restricted to arbitrary periods of time due to limitations placed by national or jurisdictional policies, health insurers or treatment providers. Despite the evidence that longer treatment with opioid agonists produces better outcomes, philosophical debates continue to arise regarding whether staying on opioid treatment for extended periods of time represents a poor outcome [5]. There is also a range of critical factors that can influence treatment cessation, which often centre on barriers to remaining in treatment. These include cost, attendance requirements, requirements to disclose treatment to employers and lack of family support. Glanz et al. speculate that treatment contracts and requirements for frequent and expensive urine drug screening, psychotherapy and follow-up visits may influence treatment discontinuation. Other studies examining the perspectives of people who receive treatment have identified cost to patients and the inconvenience of travelling for regular supervised dosing as critical factors in treatment discontinuation [6, 7]. Experiences of stigma and discrimination that are commonplace when receiving opioid agonist treatment can also contribute to decisions to leave treatment or perhaps shift to treatment modalities that are less ‘observable’ [8, 9]. Individually and combined, these factors can often influence treatment duration despite evidence suggesting that treatment cessation will increase overdose and other risk. Importantly, the majority of these factors are not inherent to the treatment itself but pertain to how treatment is delivered, and represent modifiable factors that could be addressed to improve treatment retention. Future research may be able to further unpack these individual circumstances and complexities and determine how we might increase treatment retention. Ultimately, it is critical that our treatment systems are set up so that people are able to remain in treatment for as long as they need, which would be determined by the patient and their treatment provider. These decisions should not be driven by arbitrary policies or due to treatment systems that make remaining in treatment too difficult. Open access publishing facilitated by Monash University, as part of the Wiley - Monash University agreement via the Council of Australian University Librarians. S.N. is a named investigator on an implementation trial of depot buprenorphine funded by Indivior. S.L. has received untied educational grant funding from Indivior. Suzanne Nielsen: Conceptualization. Sarah Larney: Conceptualization.

Removal of Image Steganography using Generative Adversarial Network

Secret messages can be concealed in ordinary media like audio, video and images. This is called as Steganography. Steganography is used by cyber attackers to send malicious content that could harm victims. Digital steganography, or steganography in photographs, is exceedingly difficult to detect. The detection of steganography in images, has been investigated in thoroughly by a variety of parties. The use of steganographic techniques to send more malware to a compromised host in order to undertake different post-exploitation operations that affect the exploited system. Many steganalysis algorithms, on the other hand, are limited to working with a subset of all potential photos in the wild or have a high false positive rate. As a result, barring any suspected image becomes an arbitrary policy. Filtering questionable photos before they are received by the host machine is a more practical policy. In this paper, a Generative Adversarial Network based model is proposed that may be optimized to delete steganographic content while maintaining the original image's perceptual quality. For removing steganography from photos while keeping the maximum visual image quality, a model is built utilizing a combination of Generative Adversarial Network (GAN) and Image Processing. In the future, utilizing a generator to synthesize a picture will become more popular, and detection of steganography in images will become very difficult. In comparison to other models that have been addressed further, the proposed model is able to give a mean square error of 5.4204 between the generated image and the cover image, as well as better outcomes based on several metrics. As a result, a GAN-based steganography eradication method will aid in this endeavor.

Racial Discrimination in Novel We Cast A Shadow (2019) by Maurice Carlos Ruffin

AbstractThis study is aimed to seek how far the novel of We Cast A Shadow (2019) by Maurice Carlos Ruffin reflected the issues of racial discrimination. Racial discrimination refers to the character who face discrimination through mistreatment and violence that make a great impact in the future. The unnamed character had been through a difficult time since he was a child. This analysis is related to the postcolonial theory proposed by Pieterse and Powell (2016). The use of theory was based on the interpretation in the text and context of the novel. The data was collected from the quotation in the novel. The data collection was in the form of sentences and paragraphs. This research was a descriptive and content analysis study. The result of the study shows that the racial discrimination of African-American character can be seen through violence to African-American and arbitrary Policy on Economic Aspect. In conclusion, in this post-civil rights movement era, African-Americans still face racial discrimination.

Evaluation of science advice during the COVID-19 pandemic in Sweden

Sweden was well equipped to prevent the pandemic of COVID-19 from becoming serious. Over 280 years of collaboration between political bodies, authorities, and the scientific community had yielded many successes in preventive medicine. Sweden’s population is literate and has a high level of trust in authorities and those in power. During 2020, however, Sweden had ten times higher COVID-19 death rates compared with neighbouring Norway. In this report, we try to understand why, using a narrative approach to evaluate the Swedish COVID-19 policy and the role of scientific evidence and integrity. We argue that that scientific methodology was not followed by the major figures in the acting authorities—or the responsible politicians—with alternative narratives being considered as valid, resulting in arbitrary policy decisions. In 2014, the Public Health Agency, after 5 years of rearrangement, merged with the Institute for Infectious Disease Control, with six professors leaving between 2010 and 2012 going to the Karolinska Institute. With this setup, the authority lost scientific expertise. The Swedish pandemic strategy seemed targeted towards “natural” herd-immunity and avoiding a societal shutdown. The Public Health Agency labelled advice from national scientists and international authorities as extreme positions, resulting in media and political bodies to accept their own policy instead. The Swedish people were kept in ignorance of basic facts such as the airborne SARS-CoV-2 transmission, that asymptomatic individuals can be contagious and that face masks protect both the carrier and others. Mandatory legislation was seldom used; recommendations relying upon personal responsibility and without any sanctions were the norm. Many elderly people were administered morphine instead of oxygen despite available supplies, effectively ending their lives. If Sweden wants to do better in future pandemics, the scientific method must be re-established, not least within the Public Health Agency. It would likely make a large difference if a separate, independent Institute for Infectious Disease Control is recreated. We recommend Sweden begins a self-critical process about its political culture and the lack of accountability of decision-makers to avoid future failures, as occurred with the COVID-19 pandemic.

Groundhog Day: research without old data and old references.

The use of older data and references is becoming increasingly disfavored for publication. A myopic focus on newer research risks losing sight of important research questions already addressed by now-invisible older studies. This creates a 'Groundhog Day' effect as illustrated by the 1993 movie of this name in which the protagonist has to relive the same day (Groundhog Day) over and over and over within a world with no memory of it. This article examines the consequences of the recent preference for newer data and references in current publication practices and is intended to stimulate new consideration of the utility of selected older data and references for the advancement of scientific knowledge. Examples from the literature are used to exemplify the value of older data and older references. To illustrate the recency of references published in original medical research articles in a selected sample of recent academic medical journals, original research articles were examined in recent issues in selected psychiatry, medicine, and surgery journals. The literature examined reflected this article's initial assertion that journals are emphasizing the publication of research with newer data and more recent references. The current valuation of newer data above older data fails to appreciate the fact that new data eventually become old, and that old data were once new. The bias demonstrated in arbitrary policies pertaining to older data and older references can be addressed by instituting comparable treatment of older and newer data and references.

COVID-19 Vaccines in India: Judicial Blind Spots in upholding the Right to Health

The COVID-19 pandemic brought to the forefront the intrinsic link between health and human rights and exposed the conflict between public health measures and individual rights and liberties. These conflicts are apparent in the context of COVID-19 vaccines as well. Vaccines were fast-tracked, given emergency approval and produced and distributed by a few manufacturers with the help of the government and multiple agencies, to control the COVID-19 pandemic. However, the lack of transparency in regulation, the formulation of arbitrary policies, unfair pricing, unequal procurement, restricted and discriminatory distribution, lack of informed consent and lack of accountability for adverse events following immunization (AEFI) also created conflicts and controversies, exacerbated inequities and violated the right to life and health. As people turned to the courts, the judiciary received appreciation for reiterating the recognition of the constitutional right to health and nudging the government’s vaccine policy in the right direction. However, a closer look at various aspects and questions before the courts reveals certain blind spots on the part of the judiciary in fully upholding the right to health. The article relies on key elements of the right to health, in particular the availability, accessibility, acceptability and quality (AAAQ) framework to analyse court orders, the government’s stand and their impact on individual and public health. This analysis is contextualized with the help of media reports, investigative journalism and interventions by civil society organisations. In presenting the right to health as an important and useful foundation for the government and the judiciary to reflect on and review the decisions and actions of the past three years, this paper seeks to lend support to efforts to ensure that the injustices and inequities of these pandemic years are not repeated.

Actuarial analysis of claim per payment event: theoretically developing estimation link between quantum mechanics and insurance modeling

In general, business, underwriting firms usually write policy terms by imposing additional deductible conditions in order to mitigate the risk of loss and discourage frivolous claims by modifying the indemnity payable by the underwriter where loss occurs. Under deductible conditions, underwriting firms will only be liable in a loss event where it becomes apparent that the loss has exceeded the deductible defined in the policy terms. The maximum accumulated number of losses retained by the insured applying deductible policy modifications is usually set as part of the terms and conditions of the policy documents. This paper develops an analytical framework for evaluating the effect of structural properties of dirac-delta on insurance risk variables with deductible clauses. The objective is to obtain models for the excess of loss random variable in a payment event. In order to achieve this and create analytically sound theoretical platform of investigating payment distribution functions, the quantum structure of dirac-delta is first examined in respect of probability density function. The import of adopting the dirac-delta function in this paper lies in its elegance to permit alternative technique to obtain analytically useful models for insurance severity beneficial to both the insured and insurer with particular reference to rate relativity deductible clause. We then obtained insurance excess of loss severity and variance for an arbitrary policy under deductible coverage conditions. As part of our contributions, theorems in respect of loss were stated and proved for underwriters to see reasons for their applications and use in policy underwriting decisions.

Top-down processes derail bottom-up objectives: a study in community engagement and ‘Slum-Free City Planning’

Abstract Participatory processes in housing policies and planning that engage urban poor communities through grassroots networks have been widely celebrated, but scholars have also scrutinized these policies for their limitations on the ground. Such scholarship has primarily focused on outcome indicators and local implementation, relegating the state to the background. This study focuses on everyday practices rather than outcomes and on multi-level rather than local-level implementation, using India’s national Slum-Free City Planning initiative, Rajiv Awas Yojana, in the mid-sized, southern city of Madurai as a case study. This paper draws from development studies literature to ‘bring the state back in’ to critically examine participatory planning with India’s urban poor. Findings illustrate how community participation ideals are sacrificed by different players for procedural expediency and bureaucratic convenience. We suggest that the deprioritizing of community participation is not an isolated deviation from policy, but it is shaped by two structural impediments embedded within urban housing policy mechanisms: the lack of federal constitutional mandates for housing and, in their absence, the proliferation of time-bound and project-based conditional grants through national housing programmes for the urban poor. Together, they result in an arbitrary policy environment and a push-pull of power between different levels of government, thereby sidelining community participation objectives.

Impulse-Based Computation of Policy Counterfactuals

<ns2:p>We propose an efficient procedure to solve for policy counterfactuals in linear models with occasionally binding constraints. The procedure does not require knowledge of the structural or reduced-form equations of the model, its state variables, or its shock processes. Forecasts of the variables entering the policy problem, and impulse response functions of these variables to anticipated policy shocks under an arbitrary policy, constitute sufficient information to construct valid counterfactuals. We show how to compute solutions for instrument rules and optimal discretionary and commitment policies with multiple policy instruments, and discuss various extensions, including imperfect information, asymmetric objectives, and limited commitment. Our procedure facilitates the comparison of the effects of policy regimes across models. As an application, we compute counterfactual paths of the U.S. economy around 2015 for several monetary policy regimes.</ns2:p>

Applying the Isabelle Insider framework to airplane security

• The Isabelle Infrastructure framework models and verifies security specifications. • Temporal logic CTL proves logical properties in the framework detecting attack paths. • A methodology for policy invalidation and refinement reveals missing assumptions. • A comparative NuSMV model implements variations of concrete airplane policies. • Isabelle's expressive Higher Order Logic allows generalizing over arbitrary policies. Avionics is one of the fields in which verification methods have been pioneered and brought about a new level of reliability to systems used in safety-critical environments. Tragedies, like the 2015 insider attack on a German airplane, in which all 150 people on board died, show that safety and security crucially depend not only on the well-functioning of systems but also on the way humans interact with the systems. Policies are a way to describe how humans should behave in their interactions with technical systems. Formal reasoning about such policies requires integrating the human factor into the verification process. In this paper, we report on our work on using logical modelling and analysis of infrastructure models and policies with actors to scrutinize security policies in the presence of insiders. An insider is a user of a system who behaves like an attacker abusing privileges thereby bypassing security controls. We model insider attacks on airplanes in the Isabelle Insider framework. This application motivates the use of an extension of the framework with Kripke structures and the temporal logic CTL to enable reasoning on dynamic system states. Furthermore, we illustrate that Isabelle modelling and invariant reasoning reveal subtle security assumptions. This results in a methodology for the development of policies that satisfy stated properties. To contrast our approach to model checking, we provide an additional comparative analysis.

Prosociality in Majority Decisions: A Laboratory Experiment on the Robustness of the Uncovered Set

AbstractSocial choice theory demonstrates that majority rule is generically indeterminate. However, from an empirical perspective, large and arbitrary policy shifts are rare events in politics. The uncovered set (UCS) is the dominant preference-based explanation for the apparent empirical predictability of majority rule in multiple dimensions. Its underlying logic assumes that voters act strategically, considering the ultimate consequences of their actions. I argue that all empirical applications of the UCS rest on an incomplete behavioral model assuming purely egoistically motivated individuals. Beyond material self-interest, prosocial motivations offer an additional factor to explain the outcomes of majority rule. I test my claim in a series of committee decision-making experiments in which I systematically vary the fairness properties of the policy space while keeping the location of the UCS constant. The experimental results overwhelmingly support the prosociality explanation.

Effects of air quality on house prices: evidence from China’s Huai River Policy

We employ a regression discontinuity (RD) design to estimate the impact of air pollution on house prices across a river that demarcates regions with and without coal-fired heating resulting from the Huai River Policy. This policy was decreed by the Chinese government in the 1950s and mandated the burning of coal for indoor heating at subsidised prices north of the Huai River. Employing quasi-experimental variation in particulate matter of 10 micrometres or less in aerodynamic diameter (PM10) generated by this arbitrary policy and a regression discontinuity (RD) design based on distance from Huai River, we estimate the local average treatment effect (LATE) to provide new evidence on the capitalisation of PM10 air pollution into house values. By using panel data covering 30 large cities on either side of the river for the period 2006–2015, we found that 1 µg/m3 (micrograms per cubic metre) reduction in average PM10 is associated with an approximately 1% increase in house prices. The results are robust to using parametric and nonparametric estimation methods, adjustment to a rich set of covariates, and using a subsample excluding first-tier cities.

On adaptive Linear–Quadratic regulators

Performance of adaptive control policies is assessed through the regret with respect to the optimal regulator, which reflects the increase in the operating cost due to uncertainty about the dynamics parameters. However, available results in the literature do not provide a quantitative characterization of the effect of the unknown parameters on the regret. Further, there are problems regarding the efficient implementation of some of the existing adaptive policies. Finally, results regarding the accuracy with which the system’s parameters are identified are scarce and rather incomplete.This study aims to comprehensively address these three issues. First, by introducing a novel decomposition of adaptive policies, we establish a sharp expression for the regret of an arbitrary policy in terms of the deviations from the optimal regulator. Second, we show that adaptive policies based on slight modifications of the Certainty Equivalence scheme are efficient. Specifically, we establish a regret of (nearly) square-root rate for two families of randomized adaptive policies. The presented regret bounds are obtained by using anti-concentration results on the random matrices employed for randomizing the estimates of the unknown parameters. Moreover, we study the minimal additional information on dynamics matrices that using them the regret will become of logarithmic order. Finally, the rates at which the unknown parameters of the system are being identified are presented.