Case Scenario: Read the case study carefully and answer the questions that follow. Decision-making in the public sector, what can government learn from decision sciences? Decision-making by public officials drives programs and policies and has a significant impact on the lives of citizens. Businesses make things and governments make decisions. From local decisions to National decisions, the impact of public sector decision-making on the lives of everyday people is significant. In contrast, private sector decision-making is likely to only impact a relatively narrow group of people - those who work at the company, those who have invested in the company, and those who buy the company's products. It is striking that many in the private sector have been embracing the evidence-based practices and tools from research of the decision sciences for 50+ years, whereas public sector uses are few and far between. Public sector decision-making can easily be influenced by political factors, which make good decision practices even more important. This difference between decision science use in private versus public sector can be attributed to several factors, including where these skills are taught and what the desired outcomes of the decisions are. Decision sciences are often taught in engineering and business schools (think operations research and linear optimization) but have an increasing presence in public affairs programs. As the teaching of decision science grows in public affairs schools, a significant challenge persists in bringing these concepts into government decision-making in part because of the large diversity of backgrounds and expertise throughout government agencies. Staff can have backgrounds in law, engineering, the natural sciences, the social sciences, communications or myriad of other types of training, as well as those formally trained in public affairs. Most government workers have limited awareness of decision sciences as an area of research; in fact, it is an area in which people generally do not seek out support because they feel qualified (or feel that they should be qualified) based on their experience in another domain. For example, a highly experienced engineer would be very comfortable making decisions about engineering issues. And this makes sense in the domain of decision science as well: when you clearly understand the factors that need to go into the decision, and it is not a complex decision, you do not necessarily need to draw on the lessons of decision science. However, for complex decisions, domain expertise is not sufficient. What is a complex decision? Decision researchers characterize complexity as follows: - decisions that have multiple criteria (things you care about are relative to this decision) and many possible alternatives; - decisions that have significant uncertainty in their outcomes; - decisions with competing viewpoints among decision-makers and/or stakeholders; - decisions with conflicting criteria (e.g., to get more of A, you will have less of B); - decisions that will have significant (size or time frame) impacts; and - decisions that will impact many people. For these types of decisions, using tools and principles from the field of decision science can lead to better decisions. Certainly, there are many public affairs decisions that have these characteristics. As an aside, many personal decisions have these features also. I will explore the first two of these in more detail below. Decisions that have multiple criteria and many alternatives are difficult. For an example, consider a situation in which a new renewable generation facility is needed for a region. Many people may want the facility near their area with the expectation that it will bring jobs and economic growth to their area. Others may not want it near them because of concerns about environmental impacts or increased traffic. Local control decision processes may conflict with state-wide goals. There may be some existing infrastructure that would lead to big differences in cost if it was placed in one location rather than another. How would a decision-maker decide which location is best? Typically, there may be a panel or a workgroup to make a recommendation. But how should this group review available evidence, seek additional evidence, account for conflicting objectives and compare the alternatives? This is where decision science can provide some guidance. Decisions with significant uncertainty also present unique challenges. In most public sector decisions, the impact of the decision can be estimated but is not certain. Politicians have famously asked for a one handed scientist to combat the testimony of uncertainty. Of course, there is always uncertainty when making estimates of potential outcomes; not formally representing that uncertainty and not accounting for it when making a decision can lead to poor decisions. But what is a "good" decision versus a "poor" decision? A basic premise of decision science is that the quality of the decision is not determined by the ultimate outcome. Rather, the quality of a decision has to do with how well it aligns with the decision-makers values. This is quite a different perception than the typical view of a "good decision." Because of uncertainty, the outcome of most decisions, except the very easy ones, is unpredictable .This distinction between a good decision and a good outcome is an ongoing struggle between how decision scientists see the world and how lay people do. But, the good news with the decision science view is that you can control the process, which means you can have a high-quality decision even when you are uncertain about the outcome. How do you create a high-quality, decision-making process? Ensuring that the decision lines up with decision-makers' values is possible by following basic steps laid out in many popular and technical textbooks and articles In public sector decisions, since the stakeholders will be impacted by the decision, it would be good decision practice for the decision-makers values to include consideration of stakeholder values. Studies have also shown advantages to including stakeholders in broader parts of the decision process; for example, by identifying alternatives that had not previously been considered. To conclude, "data-driven decision-making" and "evidence-based decision-making" are hot topics these days, but many who use these terms are hard pressed to say what they mean. Good decision scientists are quick to reinforce a fundamental principle in decision science: that no analysis, decision analysis included, can tell you what the best alternative is. Analysis is meant to inform decision-making by providing insights about potential outcomes and uncertainties and by clarifying what the implications of any particular decision could be. Having these tools could very well increase agreement among stakeholders, or at least elucidate exactly where the disagreements are based. As mentioned earlier, they may also help identify new, preferred alternatives. These tools can also be a great help to inform efforts to communicate with people outside of the decision process about why this alternative was selected. In sum, decision analysis approaches can provide structure, consistency, transparency and understanding about public sector decisions, which would benefit the public as well as the decision maker.
Question: This distinction between a good decision and a good outcome is an ongoing struggle between how decision scientists see the world and how lay people do. As alluded to in the narrative, a number of tools and techniques can be a great help to inform efforts to communicate with people outside of the decision process about why this alternative was selected. Critically discuss the tools or techniques that managers can use in the decision making process.