Section 1. General Principles. Openness in government strengthens our democracy, promotes the delivery of efficient and effective services to the public, and contributes to economic growth. As one vital benefit of open government, making information resources easy to find, accessible, and usable can fuel entrepreneurship, innovation, and scientific discovery that improves Americans' lives and contributes significantly to job creation.
Decades ago, the U.S. Government made both weather data and the Global Positioning System freely available. Since that time, American entrepreneurs and innovators have utilized these resources to create navigation systems, weather newscasts and warning systems, location-based applications, precision farming tools, and much more, improving Americans' lives in countless ways and leading to economic growth and job creation. In recent years, thousands of Government data resources across fields such as health and medicine, education, energy, public safety, global development, and finance have been posted in machine-readable form for free public use on Data.gov. Entrepreneurs and innovators have continued to develop a vast range of useful new products and businesses using these public information resources, creating good jobs in the process.
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To promote continued job growth, Government efficiency, and the social good that can be gained from opening Government data to the public, the default state of new and modernized Government information resources shall be open and machine readable. Government information shall be managed as an asset throughout its life cycle to promote interoperability and openness, and, wherever possible and legally permissible, to ensure that data are released to the public in ways that make the data easy to find, accessible, and usable. In making this the new default state, executive departments and agencies (agencies) shall ensure that they safeguard individual privacy, confidentiality, and national security.
Sec. 2. Open Data Policy. (a) The Director of the Office of Management and Budget (OMB), in consultation with the Chief Information Officer (CIO), Chief Technology Officer (CTO), and Administrator of the Office of Information and Regulatory Affairs (OIRA), shall issue an Open Data Policy to advance the
management of Government information as an asset, consistent with my memorandum of January 21, 2009 (Transparency and Open Government), OMB Memorandum M-10-06 (Open Government Directive), OMB and National Archives and Records Administration Memorandum M-12-18 (Managing Government Records Directive), the Office of Science and Technology Policy Memorandum of February 22, 2013 (Increasing Access to the Results of Federally Funded Scientific Research), and the CIO's strategy entitled "Digital Government: Building a 21st Century Platform to Better Serve the American People." The Open Data Policy shall be updated as needed.
(b) Agencies shall implement the requirements of the Open Data Policy and shall adhere to the deadlines for specific actions specified therein. When implementing the Open Data Policy, agencies shall incorporate a full analysis of privacy, confidentiality, and security risks into each stage of the information lifecycle to identify information that should not be released. These review processes should be overseen by the senior agency official for privacy. It is vital that agencies not release information if doing so would violate any law or policy, or jeopardize privacy, confidentiality, or national security.
(a) Within 30 days of the issuance of the Open Data Policy, the CIO and CTO shall publish an open online repository of tools and best practices to assist agencies in integrating the Open Data Policy into their operations in furtherance of their missions. The CIO and CTO shall regularly update this online repository as needed to ensure it remains a resource to facilitate the adoption of open data practices.
(b) Within 90 days of the issuance of the Open Data Policy, the Administrator for Federal Procurement Policy, Controller of the Office of Federal Financial Management, CIO, and Administrator of OIRA shall work with the Chief Acquisition Officers Council, Chief Financial Officers Council, Chief Information Officers Council, and Federal Records Council to identify and initiate implementation of measures to support the integration of the Open Data Policy requirements into Federal acquisition and grant-making processes. Such efforts may include developing sample requirements language, grant and contract language, and workforce tools for agency acquisition, grant, and information management and technology professionals.
(c) Within 90 days of the date of this order, the Chief Performance Officer (CPO) shall work with the President's Management Council to establish a Cross-Agency Priority (CAP) Goal to track implementation of the Open Data Policy. The CPO shall work with agencies to set incremental performance goals, ensuring they have metrics and milestones in place to monitor advancement toward the CAP Goal. Progress on these goals shall be analyzed and reviewed by agency leadership, pursuant to the GPRA Modernization Act of 2010 (Public Law 111-352).
(d) Within 180 days of the date of this order, agencies shall report progress on the implementation of the CAP Goal to the CPO. Thereafter, agencies shall report progress quarterly, and as appropriate.
Sec. 4. General Provisions. (a) Nothing in this order shall be construed to impair or otherwise affect:
(i) the authority granted by law to an executive department, agency, or the head thereof; or
(c) This order is not intended to, and does not, create any right or benefit, substantive or procedural, enforceable at law or in equity by any party against the United States, its departments, agencies, or entities, its officers, employees, or agents, or any other person.
(d) Nothing in this order shall compel or authorize the disclosure of privileged information, law enforcement information, national security information, personal information, or information the disclosure of which is prohibited by law.
Using a step-by-step decision-making process can help you make more deliberate, thoughtful decisions by organizing relevant information and defining alternatives. This approach increases the chances that you will choose the most satisfying alternative possible.
As you collect information, you will probably identify several possible paths of action, or alternatives. You can also use your imagination and additional information to construct new alternatives. In this step, you will list all possible and desirable alternatives.
Once you have weighed all the evidence, you are ready to select the alternative that seems to be best one for you. You may even choose a combination of alternatives. Your choice in Step 5 may very likely be the same or similar to the alternative you placed at the top of your list at the end of Step 4.
In this final step, consider the results of your decision and evaluate whether or not it has resolved the need you identified in Step 1. If the decision has not met the identified need, you may want to repeat certain steps of the process to make a new decision. For example, you might want to gather more detailed or somewhat different information or explore additional alternatives.
In psychology, decision-making (also spelled decision making and decisionmaking) is regarded as the cognitive process resulting in the selection of a belief or a course of action among several possible alternative options. It could be either rational or irrational. The decision-making process is a reasoning process based on assumptions of values, preferences and beliefs of the decision-maker.[1] Every decision-making process produces a final choice, which may or may not prompt action.
Decision-making can be regarded as a problem-solving activity yielding a solution deemed to be optimal, or at least satisfactory. It is therefore a process which can be more or less rational or irrational and can be based on explicit or tacit knowledge and beliefs. Tacit knowledge is often used to fill the gaps in complex decision-making processes.[3] Usually, both of these types of knowledge, tacit and explicit, are used together in the decision-making process.
A major part of decision-making involves the analysis of a finite set of alternatives described in terms of evaluative criteria. Then the task might be to rank these alternatives in terms of how attractive they are to the decision-maker(s) when all the criteria are considered simultaneously. Another task might be to find the best alternative or to determine the relative total priority of each alternative (for instance, if alternatives represent projects competing for funds) when all the criteria are considered simultaneously. Solving such problems is the focus of multiple-criteria decision analysis (MCDA). This area of decision-making, although long established, has attracted the interest of many researchers and practitioners and is still highly debated as there are many MCDA methods which may yield very different results when they are applied to exactly the same data.[5] This leads to the formulation of a decision-making paradox. Logical decision-making is an important part of all science-based professions, where specialists apply their knowledge in a given area to make informed decisions. For example, medical decision-making often involves a diagnosis and the selection of appropriate treatment. But naturalistic decision-making research shows that in situations with higher time pressure, higher stakes, or increased ambiguities, experts may use intuitive decision-making rather than structured approaches. They may follow a recognition-primed decision that fits their experience, and arrive at a course of action without weighing alternatives.[6]
The decision-maker's environment can play a part in the decision-making process. For example, environmental complexity is a factor that influences cognitive function.[7] A complex environment is an environment with a large number of different possible states which come and go over time.[8] Studies done at the University of Colorado have shown that more complex environments correlate with higher cognitive function, which means that a decision can be influenced by the location. One experiment measured complexity in a room by the number of small objects and appliances present; a simple room had less of those things. Cognitive function was greatly affected by the higher measure of environmental complexity, making it easier to think about the situation and make a better decision.[7] 152ee80cbc
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