Policy studiesPolicy studies is a subdiscipline of political science that includes the analysis of the process of policymaking (the policy process) and the contents of policy (policy analysis). Policy analysis includes substantive area research (such as health or education policy), program evaluation and impact studies, and policy design. It "involves systematically studying the nature, causes, and effects of alternative public policies, with particular emphasis on determining the policies that will achieve given goals.
Instrumental convergenceInstrumental convergence is the hypothetical tendency for most sufficiently intelligent beings (human and non-human) to pursue similar sub-goals, even if their ultimate goals are pretty different. More precisely, agents (beings with agency) may pursue instrumental goals—goals which are made in pursuit of some particular end, but are not the end goals themselves—without ceasing, provided that their ultimate (intrinsic) goals may never be fully satisfied.
Biological neuron modelBiological neuron models, also known as a spiking neuron models, are mathematical descriptions of the properties of certain cells in the nervous system that generate sharp electrical potentials across their cell membrane, roughly one millisecond in duration, called action potentials or spikes (Fig. 2). Since spikes are transmitted along the axon and synapses from the sending neuron to many other neurons, spiking neurons are considered to be a major information processing unit of the nervous system.
AI alignmentIn the field of artificial intelligence (AI), AI alignment research aims to steer AI systems towards humans' intended goals, preferences, or ethical principles. An AI system is considered aligned if it advances the intended objectives. A misaligned AI system pursues some objectives, but not the intended ones. It can be challenging for AI designers to align an AI system because it can be difficult for them to specify the full range of desired and undesired behaviors.
Binary optionA binary option is a financial exotic option in which the payoff is either some fixed monetary amount or nothing at all. The two main types of binary options are the cash-or-nothing binary option and the asset-or-nothing binary option. The former pays some fixed amount of cash if the option expires in-the-money while the latter pays the value of the underlying security. They are also called all-or-nothing options, digital options (more common in forex/interest rate markets), and fixed return options (FROs) (on the NYSE American).
Social policySocial policy is a plan or action of government or institutional agencies which aim to improve or reform society. Some professionals and universities consider social policy a subset of public policy, while other practitioners characterize social policy and public policy to be two separate, competing approaches for the same public interest (similar to MD and DO in healthcare), with social policy deemed more holistic than public policy. Whichever of these persuasions a university adheres to, social policy begins with the study of the welfare state and social services.
Probability distributionIn probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment. It is a mathematical description of a random phenomenon in terms of its sample space and the probabilities of events (subsets of the sample space). For instance, if X is used to denote the outcome of a coin toss ("the experiment"), then the probability distribution of X would take the value 0.5 (1 in 2 or 1/2) for X = heads, and 0.
Joint probability distributionGiven two random variables that are defined on the same probability space, the joint probability distribution is the corresponding probability distribution on all possible pairs of outputs. The joint distribution can just as well be considered for any given number of random variables. The joint distribution encodes the marginal distributions, i.e. the distributions of each of the individual random variables. It also encodes the conditional probability distributions, which deal with how the outputs of one random variable are distributed when given information on the outputs of the other random variable(s).
PolicyPolicy is a deliberate system of guidelines to guide decisions and achieve rational outcomes. A policy is a statement of intent and is implemented as a procedure or protocol. Policies are generally adopted by a governance body within an organization. Policies can assist in both subjective and objective decision making. Policies used in subjective decision-making usually assist senior management with decisions that must be based on the relative merits of a number of factors, and as a result, are often hard to test objectively, e.
Public policyPublic policy is an institutionalized proposal or a decided set of elements like laws, regulations, guidelines, and actions to solve or address relevant and real-world problems, guided by a conception and often implemented by programs. The implementation of public policy is known as public administration. Public policy can be considered to be the sum of a government's direct and indirect activities and has been conceptualized in a variety of ways. They are created and/or enacted on behalf of the public typically by a government.
Gradient descentIn mathematics, gradient descent (also often called steepest descent) is a iterative optimization algorithm for finding a local minimum of a differentiable function. The idea is to take repeated steps in the opposite direction of the gradient (or approximate gradient) of the function at the current point, because this is the direction of steepest descent. Conversely, stepping in the direction of the gradient will lead to a local maximum of that function; the procedure is then known as gradient ascent.
Deep reinforcement learningDeep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem of a computational agent learning to make decisions by trial and error. Deep RL incorporates deep learning into the solution, allowing agents to make decisions from unstructured input data without manual engineering of the state space. Deep RL algorithms are able to take in very large inputs (e.g.
