Structured Decision Making and RAD

0:05
Yeah, thanks everybody and thanks for coming.
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Recognize a lot of the names in the in the attendees.
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Appreciate that.
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So, yeah, I'm kind of excited to to talk about this today.
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Some thoughts about how do we marry these two paradigms.
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And I, I mean, the, the message I'm going to give is I, I think, I think it's easy, right?
0:28
Famous last words, but I, I think that there's some really natural connections to be between these two paradigms.
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RAD and SDM are both designed to address difficult decisions when the system is changing, right?
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They both recognize these challenges and are, are frameworks to aid decision makers in these very difficult settings.
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And So what I really want to talk about today is what do each of these frameworks contribute and how can they contribute to each other?
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I'm going to actually start by taking a step back to to say, look, why do we even need any kind of framework or structure?
1:06
Why do we need SDM or RAD or anything?
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Why can't we just go about our business and make the decisions we need to make?
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So I'm going to start there and then, and then I'm going to talk about, I want to ask the question, what is SDM contribute structure decision making, contribute to RAD, the resist accept direct framework.
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And then I'll come around to ask, well, what does the RAD framework contribute to structure decision making?
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And then at the end I'll, I'll sort of reflect on where is the cutting edge of this field and, and what's coming next.
1:37
OK, so I, I want to start in a place that maybe you weren't expecting, which is why do we even need either of these things?
1:45
Why do we need SDM or RAD or any kind of framework to help us in decision making?
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Well, another way to ask that question actually is left to our own devices.
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How do we as humans make decisions?
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If we don't have some kind of structured process, how do we make decisions?
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So in 2002, the Nobel Prize in Economics was awarded to Daniel Kahneman for work that he did over the course of his lifetime with Amos Tversky.
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Amos Tversky passed away before this award was given, otherwise he surely would have shared it with Daniel Kahneman.
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And they studied how people make decisions.
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There's this book that, that you can find.
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It's written for a general audience.
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It's, it's pretty readable.
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It's very interesting.
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It's called Thinking Fast and Slow.
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And, and in it, Daniel Kahneman talks about basically his life's work with Amos Tversky and, and, and what they discovered about the human mind.
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One of the central things they talk about is that we have two modes.
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We one, which is called system 1 is sort of our fast mode of thinking.
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It's where our intuition is.
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It's where we make snap decision making.
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If you've heard of the book that was around about 15 years ago called Blink, which argued that like our snap decisions are good, right?
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This is, this is about system one, these fast methods we have in our head for making decisions.
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System 2 is the slow method.
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It's our deliberative reasoning.
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And we can do both of these things and we can switch between them.
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And so that was sort of like that, that understanding or that characterization of human modes of thinking and, and decision making was sort of the basis of what they did.
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And then they spent a lot of time actually studying system 1.
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And so system one, our, our snap decision making works with evolved heuristics.
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So we've evolved as decision makers, right?
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And in environments where we needed to make decisions quickly.
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You know, I always think about running away from a sabretooth tiger, right?
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You don't have a lot of time to do a structured decision making process for that.
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So we have these evolved heuristics in our mind that are ways to make decisions quickly, right?
4:09
That presumably these heuristics are adaptive in the environments in which they evolved, but the trick is we're now applying them in environments in which we did not evolve.
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We did not evolve in complex hierarchical government agencies, right?
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That's not part of our evolutionary pressure.
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So interestingly, or, or any of the other aspects of modern life, right, So these heuristics can lead to cognitive biases.
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They are shortcuts.
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They're shortcuts to decision making.
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There's shortcuts to inference.
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They can lead to cognitive biases.
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And, and so a lot of what Tversky and and Kahneman did was sort of create this field of study of all these cognitive biases.
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This this diagram, which I pulled off the Internet, I don't know I mean, 5 or 6 years ago, so it's surely outdated lists hundreds of cognitive biases that people have identified and studied and done experiments on people in cognitive psychology to see how these these heuristics and biases work.
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And so there's ways that we can make decisions that are bad for ourselves.
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It when we engage this system.
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One thinking some relevant ones I want to mention today are status quo bias, egocentrism, and positive illusions.
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So these biases, status quo biases bias is 1 in which, you know, a simple way to make a decision is just to keep doing what you've been doing, right?
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If it worked yesterday, it should work tomorrow, right?
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And so that that is that sort of ingrained as a shortcut for us and explains an awful lot about why there's often a lot of inertia for us as we're making decisions.
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We're like, why, why should I change?
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Why, why should I refinance my mortgage?
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My mortgage is fine, right?
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Or any kind of sort of status quo bias.
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Second one, egocentrism is a tendency for us to focus on our own needs, our own ego needs and our own own particular values.
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Even if we have a larger set of values that we care about.
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We're sort of in ingrained with sort of this self preservation ethic that can intuitively guide our decision making even if that's not what we want.
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We also have this bias called positive illusions, which is sort of the idea that as long as we're in control of something, something will, it will probably come out OK, right?
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That we have this positive illusion about how much we can affect the world.
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So interestingly, I think this is one that's sort of at play in the, you know, one of the responses to climate change in some sectors is like, look, don't worry about it, we'll figure it out.
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Technology will save us, right?
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This is sort of a, a over.
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It's a confidence, right?
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Ingrained confidence that we'll be able to figure it out.
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Maybe we will, maybe we won't.
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But like that's not necessarily sort of a rational argument.
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I mentioned these three, these are ones that Madeleine Rubenstein pointed out in a paper that has just been accepted about four days ago in conservation biology called cognitive biases and institutional barriers and time dependent decision making.
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And what Madeleine's thinking about in this paper is what are these cognitive challenges we face specifically in the context of climate adaptation and other kinds of decision making in non stationary systems, right?
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What are the particular cognitive biases?
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And in a moment I'll talk about institutional barriers that that sort of are triggered in these settings.
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So look for that.
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It's a, it's a pretty great, it's a pretty awesome paper and I'm excited to see that one come out pretty soon.
7:54
So, OK, so these are the, these are the things that are at play when we are making decisions as individuals, right?
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What about when we're making decisions in groups?
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Well, when we make decisions in groups, say you're on a town committee, you're on the town conservation committee, right?
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Or you're whatever on some, some appointed advisory panel in your job.
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In groups, all the cognitive biases of individuals are still at play.
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And there's some more, right?
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There's some new ones that are added.
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And these kind of go under the, the rubric of groupthink and the, the general idea that there's a lot of specific things under this category, but the general idea of groupthink is that it's sort of a failure.
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Tap into the full knowledge present within a group because of group dynamics.
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So within this group, you have a lot of knowledge, but the group dynamics can create this myopia where you focus too quickly on a narrow set of possibilities and leave out possibilities that people in the group can think of but don't articulate right.
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And there's a lot of really prominent examples of where groupthink has led to some catastrophic failures in things.
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Feel free to Google it and you'll find all of those examples that people have talked about.
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It's exacerbated by a homogeneous composition of the group, by group cohesiveness, by authoritarian leadership, by urgency of the situation.
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There's a bunch of factors that can make this work.
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But at any rate, when in groups, we're prone to even more bias than when we're making decisions alone.
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What's worse is OK, how, how do humans make decisions in institutions?
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So there's a lot of study also on institutional behavior and how decisions happen in institutions.
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So institutions create and, and Madeleine talks about this in her paper as well.
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Institutions create their own barriers to good decision decision making.
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I'll mention three that that Madeleine talks about proceduralism, policy rigidity and escalation of commitment.
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So bureaucracies like to create procedures.
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Now procedures are a means to an end, right?
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They're a way to think about how can we achieve the outcomes that are institutions meant to achieve, But like, how can we make it efficient?
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And we create these procedures, but then when the when the means become ends themselves, when we get focused on proceduralism, it creates this policy rigidity.
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We forget why we're doing what we're doing and we just follow the rules and do it.
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And so this policy rigidity is problematic in a changing environment, right, Because you may specifically need the institution be able to to change its practices, but the behavior of the institution has this embedded rigidity in it.
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Another, another bicep or institutional barrier that comes up is escalation of commitment.
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This also happens on the individual level.
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Once we've started something and we've started to commit resources to a particular course of action, we tend to double down on it even in the face of evidence that it's not working.
11:12
OK.
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My point is the deck is kind of stacked against us, right?
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Left to our own devices, the deck is stacked against us these by because these biases are particularly acute in non stationary systems, again, which is not how we evolved, right?
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And so we need to think about then how do we get out of this trap?
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How do we make good decisions in the face of cognitive biases, in the face of groupthink, in the face of institutional barriers?
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2 answers to this question come up when people ask it.
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One is, well, just awareness of these innate tendencies is important.
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The more we're aware of what these cognitive biases are about, the properties of groupthink, institutional barriers, maybe we can reflect on that, recognize them in ourselves and maybe that's a way to break out of them.
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The second recommendation is you structure processes to guard against them, processes that are designed to help get you out of the trap of these biases, right?
12:07
And so I think that so OK, why do we need SDM and RAD?
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We need SDM and RAD because left to our own devices, we may not be good, we may not be making good decisions and these are ways to help us get there.
12:21
OK, so, so with that is Prelude, I guess I, I just want to know right that this is this whole field of descriptive decision analysis of cognitive psychology that I just talked about.
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There's people have thought a lot about how humans make decisions and, and where they can go wrong.
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So what do we do in the face of that?
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What does structured decision making specifically offer in the face of those cognitive biases?
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And what does it offer to the resist accept direct framework And I and my my argument, the short answer of the next 10 minutes, I'm going to, you know, the the shortened version of the next 10 minutes is SDM offers a process for decision making, offers rad a process for decision making.
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So let me elaborate on that.
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So there is this field called normative decision theory, and it stems from an interesting question, a philosophical question really, which is how should a rational person acting in their own interests make decisions?
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What would that look like?
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It's not to say you have to do this, but if you're a rational person acting in your own interests, what would be the optimal way to make decisions?
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That that's kind of the genesis of this whole field.
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The the tenets of this theoretical field were established before there was a Nobel Prize in Economics.
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And you know some some of the luminaries.
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Frank Knight in his dissertation in 1921, Frank Ramsey in 1931, Von Neumann and Morgenstern in 1944, Leonard Savage 1954.
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These were some extraordinary Seminole works that likely would have won the Nobel Prize in Economics had it existed when this work was done.
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It's they, they established this field of normative decision theory that that talks about, you know, how should make decisions in the face of uncertainty, How should make decisions when there's multiple objectives.
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If you're playing a game against somebody that you're competing against, how do you make the smartest decisions in the, in the face of that competition?
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Trying to derive on 1st principles what that decision theory would look like.
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So structured Decision Making, which is just sort of like a a more modern name for the practice of normative decision theory, outlines 2 really foundational tenets of decision theory.
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1 is a problem, not decomposition, and one is values focused thinking.
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The idea of problem decomposition is that any decision can be broken down into the same 5 elements, and that by decomposing it in this way, you can analyze the pieces in a structured way.
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And then you can put the pieces back together to arrive at sort of the rational logic that would, you know, lead you to a choice of action.
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Those elements are a a problem definition of problem framing objectives.
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That is the the long term outcomes you hope to achieve, the fundamental things you hope to achieve a set of alternatives from which you can choose a way of predicting consequences.
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That is a way of predicting how well each of the alternatives will achieve each of the objectives and then some from, you know, some way of evaluating the trade-offs that arise out of that analysis to choose the action that you want to implement.
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We we refer to this, we use an acronym proact to refer to this, the set of elements.
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But this, this, this probably composition is the sort of a helpful way to sort of wade into a really complex decision and start to get your hands around the pieces of it.
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The second tenet is values focused thinking, which is the idea that any decision that we make is an attempt to achieve something we value that values are inherent to decisions.
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That is the objectives that are fundamental to you, the outcomes you fundamentally want to achieve
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the decision.
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So those values, those objectives need to be front and center in any kind of decision analysis.
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And you know, the interesting side side note, the interesting thing is we often talk in our field about, well, we want to make a science based decision.
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You can't make a decision that is based only on the science, right?
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The the idea of decision theory is that you also have to integrate the values that you want to achieve through this decision in order in order to actually make a rational decision.
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OK.
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So these are the tenets.
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Let me, I, I want to talk a little bit about a couple of these elements a little bit more because these are questions that come up in any RAD application as well, right?
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And, and what I want to argue is that what decision theory gives us, what structured decision making gives us, is a process for how to answer these tough questions that any that need to be answered for any decision, whether it's around decision or not, the step of problem definition is really critical.
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One of the questions we asked at this stage of an analysis is who is the decision maker and what is their authority to act?
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That's an extraordinarily, surprisingly, an extraordinarily hard question to answer.
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But if you can't answer that question, you cannot proceed with a decision analysis that makes any sense, right?
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You'll be solving the wrong problem if you can't answer this decision.
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So who is it that is making the decision and what authority do they have?
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What is the scope of their what is their jurisdiction to act?
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That's really a really central question.
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And and I think a lot of times there are scenario planning exercises that people will go through, you know, sort of conservation planning, resource management planning exercises people will go through and they'll do all kinds of fancy analysis, but they won't have other answered this question.
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So it's not clear who their product is designed for.
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The second, you know, second question at the problem definition stage is who are the stakeholders or rights holders or interested parties?
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What role will they play in this decision making process?
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How are they affected by the decision and how can they affect the decision?
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What role will they play?
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A lot of stuff is really important to sort out pretty early on.
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The objectives, as I mentioned, these, these, these, the question here is what are the fundamental outcomes being sought?
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Often there's multiple outcomes that we care about.
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And then an embedded piece of this is what metrics are we going to use to measure achievement of those objectives?
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And this is actually really hard.
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Sometimes we can, sometimes we can articulate the fundamental objectives, but we don't know how to measure achievement of them.
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Sometimes what what we do is it's hard for us to articulate the fundamental objectives.
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So we just make up some measures, but the measures might not actually reflect what we care about.
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So there's some some real complexity here to to figure out like what are we trying to achieve and how are we going to measure that achievement?
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That becomes really important in, in all the later steps.
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I mean, it's hard to build a model for the effects of your actions unless you know what you're trying, the outcomes you're trying to achieve.
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Then the alternatives, what actions can the decision maker choose from?
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And and often as I'll talk about here in a few minutes.
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This is a really important creative process.
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How can we dream up possible ways to proceed?
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OK, so you know, I, I my point here I guess is that RAD needs these questions answered as does any SDM process.
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And SDM provides a bunch of ways facilitation devices to to talk to the decision makers and stakeholders about these kind of questions.
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At the latter stages, structured decision making provides a whole bunch of tools, rich set of analytical tools for helping decision makers overcome specific cognitive challenges in the decisions.
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So in natural resource management, a lot of times what we have, in fact almost always what we have is multiple objectives and they compete with each other.
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So how do we manage the trade-offs among those objectives to find the action that just balances things?
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Well, there's a field called multi criteria decision making that provides a lot of both facilitation tools and analytical tools for grappling with that kind of question.
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What if we're making a decision in the face of uncertainty?
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The field risk analysis provide us tools there.
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What if we have to assemble a portfolio of actions, a complex portfolio of actions?
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How do we assemble that portfolio and and to best achieve our objectives?
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There's a field of portfolio analysis.
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What if we're trying to figure out what research would would improve decisions in the long term?
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There's techniques called value of information.
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There's a field called dynamic programming for when we've got a sequence of decisions to make.
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My point is there's a lot of really interesting quantitative tools that also come with a sort of a lot of conceptual insights to help decision makers, you know, at the kind of gnarly bit of all right, fine, we've done this analysis, but how do I choose the best action?
22:07
So, so that, so, OK, so these are the, these are kind of the aspects of normative decision theory.
22:14
What's grown up in the last, I think 30 or 40 years is a lot of facilitation tools to accompany the analytical aspects, right.
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So SDM provides really an approach for facilitating the decision process as well as it gives us a, a specific sequence of steps to take.
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It gives us a specific set of elements to develop.
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It gives us elicitation tools for both the scientific and policy elements.
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How do we, how do we elicit the scientific judgment from scientific experts?
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How do we elicit the value judgments that are needed from policy makers in ways that are that sort of reflect the true underlying values?
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So a lot, a lot of process stuff that goes along with that.
23:05
OK, so, so I think that I think that really then this is, this is, this is what SDM contributes to RAD, right?
23:14
It's, it's already established.
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You don't have to reinvent a process for decision making.
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There's, there's one that's been well developed.
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It's got 100 years of history.
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It's got tons of tools and a lot of insights.
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And I think that that then becomes sort of an undergirding that you can practice a sort of RAD in and it gives you that process.
23:38
OK, flip, flip question here.
23:42
What does RAD contribute to SDM?
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This is a really, really interesting question.
23:51
I and the short answer of my the next 10 minutes is I think it gives us a framework for creative thought in the face of non stationarity.
23:59
And that's important, right?
24:02
So, OK, the hidden assumption here, right?
24:05
The hidden assumption between an awful lot of natural resource management is the assumption of stationarity that the system tomorrow will work like it did yesterday, right?
24:18
That, I mean, This is why we're here, right?
24:19
This is, you know, This is why RAD exists, is because we want to challenge this assumption.
24:26
Now, in theory, structured decision making does not assume stationarity.
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There's nothing actually in decision theory that says that that the system dynamics have to be stable, have to be stationary.
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But in practice, a lot of decision making and a lot of decision analysis does implicitly assume stationarity.
24:50
And that's really because we're so accustomed to using past experience to forecast future outcomes.
24:56
We're we're so used to using past experience to even frame the question, to even frame the decision that we think we have, that there's an awful lot of hidden assumption of stationarity that we don't even know what's going on.
25:14
So I think one of the things that incidentally, if you want to know what this picture is, it's it's just here for, you know, fancy display.
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This is the sea ice extent in the Arctic.
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This was work that we were using in in 2007 when polar bears were were being considered for listing.
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And it was it's just showing that the forecast from the climate models of sea ice extent illustrating non stationarity.
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So the thing I think that RAD really brings to the fore, of course, right is non stationarity, non stationarity system change as the central focus.
25:50
This is the central concern and I think that that and what it does really well is, is center that in the conversations and, and, and I think that's a benefit to structured decision making processes.
26:07
I think there's three specific things that RAD can do in a structured decision making process.
26:12
The 1st and I think the most obvious and I think the one people have talked about most is it leads to creative generation of alternatives.
26:20
So reflecting back on one of the cognitive biases, status quo bias that I mentioned earlier is, you know, we're often not very creative in thinking about the possible actions we could take for resource management.
26:40
And, and really there's a, there's a quote from one of the, you know, old decision analysts when the one of the guys from the 60s that says a decision is only good as the set of alternatives you have, right?
26:58
The you, you can, you can only choose from the set of alternatives that you're considering.
27:06
And so one way to make your decisions better is to have a wide array of alternatives, a more creative array of alternatives.
27:13
And I think that, you know, if you understand the RAD options resist, accept direct as alternative actions or alternative strategies, alternative portfolios of actions, then I think what this does and I think in practice, in RAD practice, this is a, you know, really a central part of it is that these are recipes for the design of management strategies for you to consider.
27:39
And they're really creative.
27:42
They're really creative ways to sort of kick us out of the status quo, right?
27:48
We're deliberately forcing decision makers out of their status quo bias to say, OK, let's think of a strategy that's a resist strategy.
27:56
What would that look like?
27:57
Let's think of an accept strategy.
27:58
What would that look like?
27:59
A direct strategy.
28:00
This is asking decision makers to think differently about the problem than they have to get a more creative set of alternatives to consider.
28:09
That's great.
28:10
That's really important.
28:13
I think there's there's a second and then a third, there's a second way in which I think RAD really enhances structured decision making or can enhance structured decision making.
28:26
And that is it's sort of a more meta level, right?
28:29
It it in some ways what happens is the non stationary of the system means that our old objectives can't be achieved anymore, right?
28:40
We don't have the tools to achieve them anymore because the system's changing and we can't, you know, we can't get what we had yesterday, right?
28:48
And that raises interesting question that invites discussion is like, OK, well, what are our objectives now?
28:54
Should are there other objectives that are important?
28:57
Or maybe our objectives all along weren't really our objectives.
29:00
They were just satisfactory sorts of things.
29:04
But maybe there's higher order of objectives we never had to think about before.
29:08
But now that we're not in the status quo, we have we we have an opportunity to reflect on what, what do we really want to achieve that's really healthy, right?
29:17
And I think Brad provides some guidance for this discussion of do you need to change your objectives?
29:23
Do you need to think about having different expectations about what this resource management is going to look like?
29:31
And that's super healthy.
29:32
So I've drawn this sort of as a back arrow in the SDM process from, jeez, you get to your analysis of consequences and you're like, wait a second, this is not what I want.
29:41
And it causes you to go back a few steps and say, what, what am I actually trying to achieve?
29:47
And, and maybe my old sense of objectives is, is no longer right.
29:53
The third thing I think that's really important, and this is actually sort of this is I'll make this point, but it's also sort of a pointer to the next talk in this webinar series in October, is that I think RAD's attention to scenario planning and horizon scanning is really important.
30:15
This attention to the long term projections under system change can trigger a conscious consideration of reframing the decisions.
30:24
That is some sense of analysis of the consequences, the predictions of what might happen may cause you to reframe the decision at at at the very outset, right?
30:35
Maybe these insights, maybe it means the spatial context is different, Like maybe you were managing for endangered species in a particularly narrow spatial area 'cause that's where it was before, but that habitat's no longer going to be suitable.
30:47
And if you care about the species, you have to look someplace else.
30:50
So maybe you need to change the spatial context.
30:53
Maybe you need to change the temporal context.
30:55
Maybe the scope of action that you're taking needs to be different than it was in the past or what you thought it was originally.
31:02
Maybe you need a different decision maker.
31:05
Maybe the decision maker that oversaw this particular, you know, issue in the past is not the right one in the future because of the way the system's changing.
31:15
Or maybe indeed you don't have the right institutional structure anymore and you have to change the institutions in order to be, or you have to change the the, the notion of who the institution, what the institutional arrangements are as part of the problem framing before you can even proceed.
31:33
So I think RAD's early focus on horizon scanning and scenario planning and its openness to adaptive governance, its openness to any of these things needing to change can inspire in an SDM process the ability to think more broadly about what you're trying to achieve.
31:51
And I think I, I think that's great.
31:53
I and so I'll just, I'll just, you know, sort of kick that topic to the to speakers in the next webinar to talk about, you know, scenario planning.
32:01
And it may be one of the ways that scenario planning can play play a role.
32:07
OK, so, all right.
32:08
So I think there's some really interesting opportunities about how RAD can enhance SDM.
32:15
Obviously when we're talking about decisions in the face of a changing system, OK, where, where is this field going next?
32:22
Well, I mean, I think the interface of these, these frameworks is really, really interesting and really pretty cutting edge.
32:28
I, I do think there's a, a cutting edge in the marriage of these paradigms.
32:32
How do we make an open optimal policy in the face of system change?
32:35
And what does it take to answer this question?
32:41
So I I do want to mention that there is a field out there, a field of study that has been grappling with this kind of question for a long time.
32:50
Although, yeah, yeah.
32:52
So there's a large subfield of decision analysis that looks at decisions that occur over time.
32:56
Some of the methods are dynamic programming and reinforcement learning.
32:59
Ken Williams, who was on the panel for the last talk, has, has developed a lot of this stuff, has done a lot of work in this area.
33:07
Most of the applications, many of the applications assume stationary, but that's not a strict requirement.
33:13
Actually the algorithms existed for a long time to solve non stationary problems.
33:19
And indeed, one of the interesting questions in this field is how do we embed learning about the system into the management so that if the system is changing, you can detect it and respond to it.
33:32
So there's algorithms for doing that kind of embedded learning.
33:37
A lot of this work is not in natural resource management, It's in robotics, it's in manufacturing, it's in, in computer science, right there.
33:46
A lot of this is, is sort of built into some AI algorithms.
33:51
There is work out there, right?
33:52
It's just that it hasn't, it really hasn't been ported over into natural resource management.
33:56
We've tried, right?
33:57
Like there's been, there's been attention to this, but not as quite as much attention on what you do in a changing system.
34:03
So there's a lot of tools there that are sort of, I think under utilized so far in our field.
34:09
Couple I, I, I want to pay, you know, there's a little self-serving, but I want to point to a couple papers recently that are starting to move in this direction.
34:16
There's a paper that Anna Tucker lead a couple years ago on from the decision analysis lens of how do we make decisions in a changing system?
34:25
Like how do we make optimal time dependent decisions in a changing system?
34:31
The paper that Abby lead from last year, that's coming from the RAD perspective about, well, how do we incorporate some of these ideas, switch points and triggers?
34:46
Like when we have to change strategies, how would we know that's the case?
34:50
How we invent that learning?
34:51
How do we, you know, what, what are the sort of decision triggers that would lead us to change RAD strategies?
34:58
So anyway, you know, there's a few, there's a handful of other papers that are that are starting to move in this direction, But that's great.
35:06
There's a lot more to do, right?
35:07
There's a lot more to explore.
35:08
We need a lot more practice integrating SDM and RAD.
35:11
I just, I'd love to see dozens and dozens of case studies so that we can start to develop guidance and insights about how they work together.
35:19
I know there was a webinar that was talking about human dimensions and rads a little while ago.
35:23
I think non stationary, the non stationary dynamics in human dimensions are, are all a part of this too.
35:31
And we need to learn more about how we embed those in these kind of decision processes, adaptive governance.
35:37
I think there's a lot of there's a lot of words out there about it, but how do we do it in practice?
35:42
Like there's a lot of areas here, I think for us to explore about the integration of these things.
35:48
And just one little opportunity here.
35:50
There's going to be a special issue of the journal Decision Analysis.
35:55
Decision analysis is the primary journal of of one of the two decision decision analysis professional societies.
36:02
It's so it's, it's very much about decision, normative decision theory, but this special issue is called Navigating non stationarity Decision analysis for the 21st Century.
36:13
There's going to be a call for proposals in published in the September issue of the journal, but you'll see it online like that.
36:21
That call for proposals will come out in the next month or so.
36:24
So an opportunity for people to submit some ideas about work they might want to do to include in this special issue.
36:29
And I think there's some really some rad possibilities there.