Transcription
In our previous videos, we looked at a more traditional approach to decision-making. So let's look at an alternative approach. This is called the garbage can model, from Cohen, Marsh, and Olson. And where the traditional approach is a step-by-step method for making decisions, the garbage can model is more of an organized anarchist view of decision-making.
So let's talk about how the garbage can model works. We have a choice arena—so essentially a place where decisions can be made. So you might think about this as a meeting. So maybe we have a board meeting, a town hall, um, in a post-secondary, we have school council. So we have a meeting that is the choice arena, and there are opportunities for choices to be made.
For example, we might have scheduled periods of time where we do strategic planning, where we make budgetary decisions, where we might decide contracts, who gets hired, what do we need in terms of personnel, what kind of expertise do we need? So there's, there's these choice opportunities that occur within this choice arena.
And so we have this garbage can—think, think about it as this bucket—and what we do is we throw things into the bucket. So we throw in decision-makers into the bucket. So we have participants who attend, uh, our meeting. And the key with the garbage can model is these participants might be fluid. So from meeting to meeting, there are different people who may or may not attend; it's constantly changing. So we throw some decision-makers into the bucket. We also throw in a bunch of problems and issues that we might want to address. We also throw into the bucket some general solutions. Maybe we have approaches we have taken, uh, to different problems in the future. We throw it all into the garbage can, and we mix it all up, and then we decide from there what's the problem we're going to tackle, what's the solution to that problem, who is going to work on it.
So when you use the garbage can model, where we use the garbage can model in more public institutions where we have more team management and, um, so in those we're using it when there are no clear procedures or policies. We are not looking at necessarily formulaic—we talked about before—structured problems, program decisions. So not really that, where we don't have set, uh, clear processes and policies, or where a lot of our solutions have been based on trial and error. So we might try something, see how it works, and learn from it.
So what does the garbage can model give you in terms of decisions or outcomes? So we could come with a resolution to solve a problem. So if we're looking at our choice arena being school council, our choice opportunities is going to be a strategic planning; we have this meeting each year. We come up with what is it we're going to focus on for the year. We throw in our decision-makers. So we have student representatives, we have faculty representatives, we have staff present to help make that decision inside that meeting. We hash out all the potential things we could address. So we could focus on classes, and we could focus on content or delivery. We could focus on student opportunities outside of class. So what kind of events are we putting on? So we can choose what kind of problem to tackle, and we can choose what kind of solution we're going to apply to it. So will we designate a group who will enact that and come up with ideas? Will we decide as the big group what we're going to focus on, how we're going to carry it out? So we then decide on problems and solutions. So we can resolve a problem within that garbage can model, but we can also do oversight with our garbage can model; that is, we can make a decision before the problem has even arisen.
So if we look at last year, for example, we're in the middle of COVID; we're trying to decide, um, what do we do in terms of possible disruptions that could occur? So COVID could be a problem that could impact classes; we might have a lack of attendance; we might have to shut down. So we can make a decision as what to do now before the problem even arises. How are we going to deal with it when it happens? We also have what is called flight, so that as we make the decision even after the problem has moved on to either a different choice arena, it's no longer an issue. So if we encounter this again in the future, what is the solution? What are we going to go about doing? So you can see, because we're throwing in people and we're throwing in problems and we're throwing in possible solutions and we're mixing it all up, it is a bit more of an anarchist view because the group collectively is deciding which problem, which solution, which people we're going to mix and match.
You can, with the garbage can model, limit who enters the can. So when it comes to those decision-makers, is it unrestricted access? So we're going to look at any problem, any choice, and we're going to involve anybody who wants to be a part of it. So think about a town hall; you can go see your city council; you can raise any issues that you have; you can propose any solutions that you have. Maybe we have a much more hierarchical process, so we have where the just the big important problems are being addressed only by the leadership. So our can restricts the decision-makers to the upper management. We might restrict the problems as well, to be the bigger problem. So we're selecting what goes into the can before we mix it all up, mix and match, and decide what problem, what solution, what people. Maybe it's more specialized access, so the experts have access to specific choices.
For example, let's say we're looking at, uh, in Florida, a building collapses, and so we bring in our pool of experts, and they are going to look at all the potential problems with the building, all the potential solutions, and so we might then be looking at, um, flight, so a decision after the problem, right? We can no longer prevent that building from collapsing, but we can prevent other buildings from collapsing and understand, uh, what happened before. So oversight and flight in that case. So you can restrict your can in terms of who is present. When you restrict it to that specialized access of experts, it will also restrict the solutions because your experts are coming in from different backgrounds—our engineers, our architects—and so their solutions are going to be, um, limited to kind of their field of expertise. So you limit the people; you also tend to limit the solutions to the problem.
Now we've been looking in these previous videos about what impacts decision-making, and so I'm just going to go ahead a bit; we'll have a separate video on artificial intelligence's role in decision-making, but just to kind of recap a bit of what we've discussed in our previous videos, let's look at decision-making at Amazon. Throughout the remaining videos we'll have this semester, we're going to be using Amazon as a case study as we look at the decisions that managers make. But since we're looking right now at the decision-making process, let's look at some of the guidelines that Amazon sets for how they make decisions.
So Amazon makes decisions, and when they make decisions, they must be to the delight of the customers. So the comment from Amazon's customers are always beautifully, wonderfully dissatisfied; a constant desire to delight customers drives us to constantly invent on their behalf. So we're going to invent ways to a solution. We want operational excellence within the organization; we want to be able to be responsive and make decisions that are quick and effective. And so Amazon has a two-pizza rule: so your committee, your team, cannot have more people in it than two pizzas can feed. So limit the size of the of the team that's looking at any particular problem. You're going to focus long-term and have an abiding optimism, so recognizing that any decisions, any recommendations, any strategies we make are only in their first steps and that we will build on them in the future.
All right, so if you're part of Amazon, we're looking at decision-making at Amazon, let's apply then what we have learned. So does Amazon prefer structured or unstructured problems? Well, if they like to invent ways to a solution and look at the first steps and bigger problems, then they prefer unstructured problems. Now, in terms of the decision-making approach, would you say they follow a more traditional approach or more of a garbage can approach? Well, it seems here like this is more of the garbage can approach, pulling a team together, and that team then can decide what problems we're going to address, which solutions we're going to apply. And if we go to the discussion we had in terms of the different decision-making styles—remember that was analytical, directive, behavioral, and conceptual—which decision-making style do you think predominates, um, throughout Amazon? Remember we looked at the level of ambiguity, how much uncertainty can we have? And if Amazon is inventing ways to a solution, if they're thinking long-term, then likely they're okay with, with some uncertainty, and in fact, we know they collect a lot of data and information to help them analyze, to help find probabilities of outcomes, even if we don't have a certainty in what will happen. So then we also have, remember the value orientation—is it more task and technical, more process-oriented, or is it more people and social? And so here we see the focus really is on delighting customers and coming up with innovation, creativity. So that would suggest which type of decision-making style? Probably more of the conceptual approach. We're going to look more at Amazon, uh, throughout these videos, um, but right now let's take from Amazon and their work in terms of artificial intelligence to help them in their operations, and let's look at AI in decision-making in our next video.