 So I think we will start till we keep to time. So thank you everyone for joining our webinar. So this is webinar two of the three-part series. The webinar series is on motivation and ability framework, which is also termed motor. Motor is a decision support tool for strategic planning and implementation. That is also the heading of our webinar series. The first webinar was conducted earlier on October the 21st and there are four talks from that webinar that are available on the website, on the IHE Delft website around motivation and ability. So around the motor framework. So there's four talks on there. They're not recorded talks yet. We will go on and actually record those talks so that they're available, but at least the PDFs are there for now. And the idea of this webinar series is to introduce motor to a wider audience and also to discuss collaboration and working together into the future around the topic of motivations and abilities and the integration of more social science applications within performance based projects. So within these technical performance based projects and this series will consist of four talks. The talks will be 15 minutes each and there will be 15 minutes for questions after that. So Yap Evers will monitor the chat room so you can write your questions in the chat box or the message box. What is it called? Yap, sorry. Is that? Call it a chat box, but you can use it to share messages. Yeah, where you can share messages and then at the end of each talk we will ask those questions to the presenter. So yeah, I think that is it and then there will be a third seminar, so our third seminar leading on from this will actually be on December the 16th and there will be three talks given there around motor and extending motor and also a discussion on the development applications and the research and the follow up from where we would actually like to take motor from here with a wider audience of collaborators. So we will move straight to the topic one within this webinar series and that is around the applications of motor and the first talk will be given by Dr. Shibley and that will be on what is termed motor applications. The title is Motor Applications Participatory Water Management in Bangladesh. So Shibley is an environmental and disaster risk management specialist. He's got a keen interest on strategic planning, policy making and action research. He completed his PhD in engineering from Koyoto University, Koyoto University with a Pacific focus on environmental and disaster risk science. So thank you Shibley and we are very much looking forward to this talk. So I'll let you go, 15 minutes and then we'll have 15 minutes of questions. Yes. Am I audible now? Yes, you are. Please go. And my slides are fine. Yeah. I think so. Yeah. Thank you. Thank you, John for your introduction and my talk will be motor application on Participatory Water Management in Bangladesh and this is part of our collaborative research and MOC, EGIS, ITDL, PRI Bangladesh and WSCC of Vietnam National University. So just to give you a little background, I want to start the Participatory Water Management context in Bangladesh and you know, like we had a traditional water management system where landlord actually coordinated or mobilized the local people to manage the, for example, the embankment, especially the regional embankment, the constructed embankment that is Syrian. But in 1950, then government abolished that during the Indian legal system in 1950 and with that evolution actually, that traditional water management practices collapsed. And after that, the ownership and all the responsibility of managing the water management shifted to the state. And there was a gradual development with the international advocacy and development of the partner towards the Participatory Water Management. And recently in 2014, government established a new rule that they called Participatory Water Management Rule and that again transferred the ownership and responsibility from the estate to the local people. And now Bangladesh Delta Plan 2100 is reforming the Participatory Water Management again. So now question is, is it implementable? And there are two, I think, very general argument or very general statement behind the Participatory Water Management practice in Bangladesh especially that implementing actors are complaining local people are very less interested to participate. And they obviously need capacity, willingness and to meet some basic physiological requirement to participate. And our research actually aims to support implementation of Bangladesh Delta Plan by informing this societal implementability context. And we adopted MODA for this. So what is MODA? I will not go into detail. I hope you have already that information from the last webinar. But from the paper of the Professor Fee, we know that what is planned is not always implementable. And implementability depends on ability, motivation and positive opportunity and trait of implementing actor. And at the same way, what is implemented is not always adaptable. Adaptability depends on the societies and it depends on ability and motivation of the community and the opportunity and the traits. So based on this pre-concept, actually they developed MODA tool and applied that in Vietnam. And we will see that this study in the following presentation, in the next presentation. But now our question is, is that MODA is adaptable or can this MODA travel from Vietnam to Bangladesh or not? And do we need further extension of the MODA or contextualization of the MODA? And if I did a little bit further, like why extension of MODA is needed? I think you already know this diagram that was presented in the Fee's paper. And in that paper we know that we can calculate the MODA score from the motivation and ability. And in the original MODA framework that motivation was defined by opportunity and trait and ability was defined by four components, financial, institutional, social and technical. But the question now is how to quantify each component? And to answer that question, actually we need a further extension of MODA. And under this current research, we are trying to extending this MODA from component level to indicator level. So I will not go very detailed into my methodology and other things. I want to show you just one, our so far draft result. I think this is our MODA extension. We actually developed an indicator framework for MODA, but that is specific to the context of societal adaptability of participatory water management in Bangladesh. And to explain this MODA framework or indicator framework, I want to start with the action and when we developed this framework, we assumed the action or we considered the action what it was mentioned in the participatory water management rules. And if you see that, like we have find that now participatory water management rules requires that participation in everyday operation, participation in routine maintenance and participation in conflict maintenance. But from the perspective of the local people, the participation means a little bit more further beyond the participatory water management rule. They want to involve in decision-making process. They also want to involve or they want to give voluntary labor. And they also want to work conflict management. Based on this action, we tried to develop how the indicator framework, like what should be the ability and motivation to perform this action. And then I will go to trigger before explaining the ability and motivation. So trigger, the trigger behind these actions should be our announcement of participatory water management plan. When the government announced a new participatory water management plan, it actually triggered the local people to think all about this. And the trend of rising water management problem, as I mentioned in the background paper, like in 1950, when government abolished the Chawindari system, the participatory water management system just collapsed, then there was a gradual development. At least that development is happening at the same time. Our water management problems are increasing and it's becoming more complex. So that's the situation, that trend of rising water management problem actually triggering people to think alternative to get involved in the water management process and operation. And of course, the recent natural disasters are also triggering. And with that trigger, now we will move to the ability and motivation, the indicators for ability. And we took the original component from the original motor framework, the FIST, financial ability, technical ability, institutional ability and societal ability. So we tried to identify how to quantify these components of ability. For example, I will not go into detail, but for example, in case of technical ability, we took knowledge and ability of the information on the new plan can be too important indicator to measure the technical ability of the community. And in case of institutional, we think supportive institution, political commitment and ownership of the water resources are important indicator for to measure the institutional ability. So this way, like we have, we try to identify the indicators, what will be helpful for us to quantify the ability. And at the same time, we try to identify the opportunities and threats which will help us to quantify the motivation. And we also tried to develop an indicator map to see which indicator is influencing what, how they are influencing the ability or how they are influencing the opportunity and threat. So this is a little bit complex. I don't want to go into detail due to the time limitation, but this kind of exercise actually helped us to develop our indicator further, to spin our indicator or to make a list of indicators to be concise and specific to the component. And then the question is, how can we apply this at field, right? So our target is to see or to explore the implementable and implementation ability or societal adaptability of water management in a coastal folder. And we, our plan is to conduct survey at the folder level and we are thinking the cyber method would be the focus group discussion at community level. And for that, like for example, if you remember, like knowledge is an indicator of technical ability and how to measure the knowledge. To measure the knowledge, we actually translated knowledge into three questions. So first question is, the first question that we asked to the, we'll ask to the local people like, how do you rate your degree of knowledge to operate water management infrastructure? For example, regulating the gate or regulator gate or sluice. The second question we'll ask related to this, how do you rate the degree of knowledge to construct and maintain small water management infrastructure? And the third one would be, how do you rate the degree of knowledge to do regular maintenance of water management infrastructure? So again, like these questions were framed based on the actions that we defined in the motor framework, and which came from the participatory water management rules. So when local people will answer this question, so we will ask them to answer or there is a measuring scale that will help them to measure their rate of their degree of knowledge. So that measuring a scale is a number based on number. So that number is actually the cardinal number and it starts from zero and the maximum score could be five. So zero means no knowledge and five means they have very complete and complete knowledge. So this is just one example. So we are trying to explain in this indicator in this way, each of this indicator in this way, and now then we will aggregate the final score and based on the weighted embrace method. And we will be calculated, already been calculated by the expert opinion, during our motor training workshop. So as I mentioned here, is it applicable? Like whether this motor indicators are applicable, we actually conducted a two-day workshop, training workshop, plus like expert interactive expert group workshop. So in that two-day training workshop, the first, in first day, we were focused on introducing the motor and train them on how to use this indicator framework. And on the second day, we actually led the participants to work on that indicator framework. We grouped the participants in seven groups, based on like small farmer, medium farmer, larger farmer and so on. And then experts actually were discussed within the group and they tried to quantify the indicator based on those questionnaire and measuring scale. And they presented their result in front of others and there was a cross discussion. And again, this group had the opportunity to revise their questionnaire, revise their scores, if they wanted to. And I will share just one raw result from them, from that training workshop. If you see the draft, you can see like it explains the aggregate score of different motor ability of different groups. And we know like the ability of different groups should be different. So our results from that experts opinion also reflect that the ability of the different group is different. And we know like this is not the actual, this might not be the actual score, might be different from the local people opinion, but still it gives us feedback that our indicators are working. And according to their opinion, the highest score came from the community-based organization and NGO group. And the lowest score, that means lowest ability score came from the group representing them as a small farmer, a small land ownership and land owner and farmer. So our next step is we want to go to the community level, and we want to conduct executive community level to do the survey and come up with a similar quantified indicators. And if you see like that, the score we will receive from the community level is matching with the score we have already received from the expert opinion, then we can also propose that interactive expert group opinion can be used as an alternative. And if you see the other group where I tried to show the more the ability score of different group, and you will see like zero means no ability and five means the highest ability to participate with comprehensive fixed capacity. So in that figure, you will see the community-based organization and NGOs show the highest capacity to participate and the small farmer are showing the lowest capacity. So this is all from our quick presentation. Our research is still progressing. And we hope to conduct the survey in the coming months. So that's all from now. Thank you very much. Thank you, Shibley. That was a very nice presentation and very well timed. We've got time for one or two questions. So Leon has posed a question. And one is, so the question one is beyond community FGD, do you also plan survey questionnaires within the communities? Do you also plan to do survey questionnaires within the communities? I see, like I think he wanted to mean whether we have planned to do household-level questionnaire survey or not. Yeah, that's probably going to be a broader question. Yeah, we are still discussing about that issue, but depending on the present pandemic situation, we are thinking like that might be difficult or not. But again, when we design the indicators or identify the indicators, we actually targeted FGD and because that will be relatively quicker. And since we are planning to implement this or use this tool at the strategic planning level, I think in that case, like FGD is then enough Okay, and the second question is, how would the results be used? How do you think the results would be used to improve participatory water management policy in Bangladesh? So what do you envision going forward with this? Yes, to answer the question, like I again want to show this framework, like if you see here, like we have explained or we have translated the ability to comprehend into different indicators. And when we will, and our movement of course or our survey actually give the measurement of each indicator, that means we will be able to know how these indicators are working there and what is the present status of each of the indicators. So based on that result, I think the implementing agency can design how to improve, for example, how to improve their ability. If they find like technical ability is very low, then they can come up with some new program how to enhance the technical ability of the local people. If the result says that institutional ability is very low and we already have new indicators, like one of the indicators, they start as the one indicator is very low, then they can calculate how to improve that indicator. So yeah, in that way, I think that this result will help improvement of our city water management. Thanks, Shibli, for that answer. We have time for another question and Mike Acosta has proposed a question asking, did you see a difference between rice farmers and rice fish farmers? Did you break it down into further than just farmers and was there a difference or? I think it seems a idea, like here it will be a little bit difficult to understand, but for our paper, we are trying to discuss or we have a plan to discuss or make a difference between rice farmer and shrimp farmer. And this is very interesting. So it's not possible to see from this graph, but from a pre-unit resultant in the trading workshop, we have found that shrimp farmer, they have a better institutional capacity than the rice farmer, because according to things like, and also the social capacity is stronger than rice farmer because they have a very good link with the implementing agency and outside network. And even the shrimp farmer has fairly a strong association. So the idea, of course, there's a difference with the rice farmer and shrimp farmer and we are trying to actually now seeing our result and to explain what are the differences. Okay. And one final question before we move on to the second is just with one minute to spare, is from Nora. And she said, did I understand correct that you want to move from a self-assessment of motor to an interactive expert assessment? And then what do you see as the advantage or the drawbacks of using an expert-based approach? I mean, self-assessment. I mean, these experts who are coming from the experts opinion, not myself. So this is their opinion. And when they actually framed that opinion or when they quantified those indicated they actually discussed in a group and they shared their result with other groups. So there was a very good cross discussion and option in that room. And after that discussion, they again went back to the group and if they wanted, they could revise their scores. It's truly reflected their opinion. So now our plan is to go to the field and talk with the local people. And then we will see how whether these like scores we will get from the local people whether that is matches with the score we have received from the expert opinion. And if we like, the tools are very close then we can make that comment here. So you're using a cross comparison approach to test if the expert analysis is correct. Yeah, this is so far we have that plan. But yeah, we will try. All right, it will be very interesting. And I would actually like to ask a final question but I will ask it to you offline about the roles of CSOs as they have high capacity but I will ask you that later so we can move on to the second talk. So thank you very much for that. That's very interesting. We will now move on to talk two. And I will say also that if you have questions about these different talks, you can also put those into the chat box and we will pose them separately. If we don't have time, we can pose those separately to the speakers as well. But they will come back to you with the answers as we're going through the different talks. So the second speaker is Kwan. And he will be giving a presentation on the motor applications and the experience from Vietnam. And that's where some motor was developed originally in Vietnam. And so Kwan's going to present that experience and then Kwan's background is currently as associate professor of Vietnam National University, coach in Min City and he's leading a newly established institute for the circular economy development. And he's also affiliated with the socio-hydrology group in the center of water management climate change. But I think that Kwan, you're actually the director now. Is that correct? I think this is, yeah. So actually, Kwan is the director now of taking over from Professor Fee at the Water Management Climate Change Institute. And he has a special interest in solving environment and related issues based on the inter and transdisciplinary study and boys strong partnership between academia, industry and government. It's a very interesting background. Kwan, we're looking very much forward to this talk. Can you just let us know, are you able to bring it up to share or should we do it in another way? Are you able to share your screen, Kwan? Yeah, yeah, I'm sorry, my screen. So it's working good. Yeah, can you see my presentation? Yes, we can, Kwan. Go ahead, thank you. Yeah, thank you, John. Hello, everyone. So it's my honor to representation in this second webinar on the MOTA series. So it's actually also the first one presentation in the first one. So it was mainly about for the MAKON data. So now I'm trying to give you some more applications of the MOTA framework that we have been working together with the different subgroup and not only our researcher, but also with our students. Yeah, so I'll leave you to the short introduction again. I think I will talk quickly about the MOTA, but also some updated, especially from Professor Phi, some new components. And yeah, and I will share with you a few examples in different sectors. The water area also infrastructures, ecologies related and urban related. And a few conclusions. Yeah, I think I will start with some questions about example, what are valuable tools for making decisions, and why we see that some plan cannot or just partly implemented or is failed in implementations. Why good solution are not always a choice? I would pay for it to be consensus in project planning and implementations that is harm to final outcome. So there's number of questions related to decision making in terms of project planning and implementations that we have to deal with during our numbers practice. Yeah, I think this one may be some of you already seen. So there are different, most of like academy or professional are looking as most likely the performance of the projects. We're using different tools like cost-benefit analysis or multi-criteria analysis robot this is making, looking at more on the performance side based on more like quantitative indicators. But then there are some other side that we need to go on so far, like implementations, abilities or adaptability, which is the next phase of the planning. So normally the professional there what should be done as the planning and authority. We are talking about government projects decide what can be done. So we depend on the availability and the society community adopt what they want and what they can because adoption. So this is a pause after the project implementations. So this is the updated motor framework from fee. So which here we put more on what is the triggers can be social economics, environmental, physical factors and the motivation can be negative, positive, positive, active, ability is now here including finance institution, technical society. And the action to be make is here can be what, when, how and who should be involved in different state of the projects. So here now in the past we use live performance, feasibility from authority or social adaptability. And recently we also includes the new angles of the motor is a popularity which is to include the business or industry groups. So in different aspect of the motor from professional authorities or social or business there could be some factors affect to this the motor. So actually this is from the last presentation from fee so you can see it back. And importantly thing the motor especially the motor mapping is can be have in a conscience buildings if you look at the horizontal direction so that difference people have different motivation. So if we know where they are and we can build the consensus. Also when you look at the vertical as this you see the ability of different groups. So we have to consider it to build the capacity. Yeah, so I'm sharing with you the number of applications in terms of this I tried the first one is about the water agricultures. This is under preparation for general submission where we try to investigate the impact of historical change in relation to the livelihood condition and family adaption. So this is a shoot get projects in the make home data where we build the project to prevent slight water into the areas. So the impact of this shoot gets make the chance of hydrological conditions. So some areas was fresh water but now it becomes slight or maybe some saliva now becomes fresh. So this changed the livelihoods of the people. So in this we conduct the motor framework based on two steps. The first is the we do the focus group discussion with you see the four pictures and satellite mesh where we use this to discuss with the four group in four area for commutes in the areas to understand more about the situation. And then based on that we develop a questionnaire and we did about more than 100 in-depth interview with different farmers in these areas. Yeah, so in this one we try to see how they understand the situation of the hydrological conditions and also the perception of farmer about different factors in terms of water crops or access to water crop, access to water crop when the project finished and access to water crop when after the projects. In this we also map the motor in different groups, how they perceive and how the farmers have their motivation and abilities in terms of after the projects, how they sustain the current crop, or are they spend the currents or they have to transform into a new crop. So you see the motivation map, motor map of different groups are quite diverse, yeah. Because also the complexity of the couple social hydrological system. The second application is related to ecology. So here we try to see them because the picture you see is the mangrove forest but actually it's not only the mangrove, there is rim there. So mangrove rims have been identified one of the potential livelihoods in the coastal area where in terms of natural conservation but also improve the local livelihoods. It's quite promising, but are these really sustainable or how the farmers are they really happy to sustain with these livelihoods. So in this we try to combine both the motor and the so-called sustainable livelihood framework. So we tried to assess different capitals from social, physical, or natural capitals and five capitals of the farmers who are living in these areas. And then we assess how people are really interested or sustain with these livelihoods. Yeah, so you see we, in this we combine the motor and the sustainable livelihood framework. So we calculate the capitals of different capitals also assessing the risk or that they feel about different aspects in terms of pollution disease or whatever. And you can also see the motor maps where you also see the people have different motivation in either sustaining the current model or the ones to change into other models. So basically in the upper part on the right side you see most of people still have highly motivated to and also ability to continue with this framework with this as a model stream models. Another application in ecological related is how we assess the impacts of what pollution from agricultural disease into the so-called Changin Ecological Reservation Areas in Mekong Delta. So there's a number of farm and agriculture practice like aquaculture where you see the red point is aquaculture and rice and other activities that transport waste water into the ramsa site. So in this one we see we try to understand how to have how farmers can change their behavior in protecting the surface water quality in the areas. So you see actually where we did a number of water sampling, quality sampling and you see the red color is polluted which is the water quality standards. So in this one we try to combine a bit like the motor framework and ecosystem service which is based on the perception of the farmers living around areas. If they really have a good perception about the importance of the natural preservation areas. Yeah, so you see here we do the interview and we see the people how the difference in terms of the buffer zones from the local from the areas to its surrounding from few hundred meters to up to 6,000 meters and you see that people who are really they they have a good you see that clearly who are living further from the areas they have less perception on the importance of the natural conservation areas. And also in this we tried to assess the difference agricultural practice. So in this one we just assembled we have another for rice farming. We also have another one for aqua culture. So we try to understand to see how farmers interest or in terms of reducing using fertilize or pesticides in terms of some improve agriculture practice more cleansed talk with more cleansed practice. But you see there's still quite a bit diverse which amongst the farmers that is reducing fertilize or pesticide also reducing pesticide is more than in fertilize maybe because of the impact of our health to the health so their motivation but they still in terms of the ability is still quite diverse. So people are not really have ability to change their agriculture practice. Another the third application is related to urban flooding areas. So this one's already public last year's about how we assess how the different stakeholders in Ho Chi Minh City respond to urban flood using different green fractures from brain harvesting or urban green space or births, pavement or green roof. So in this we survey and we do an interview with different stakeholders. Actually this project is a part of one of our master students. This is the other times. So we try to see the motivation of selecting different retrofitting measures from conventional drainage to some others more green infrastructures. So you see that also quite diverse or different group mostly they are supporting the motivation is supporting the green infrastructure. But they are still very much still very much prefer some very conventional one which is most likely extending the drainage system. So in some maps kind of what could be the for in the first stage they're now still convention drainage system maybe in the middle term there would be like local detentions measure but rain harvesting or green roof would be a bit further with more long term. So the perception on green fractures from different group from local government to mass media to other groups are quite different. This is another case where we also apply the MOTA framework to see how the private sectors interest in blue, green infrastructures. In this we actually did also thesis from Kailati from ICDF and Iramot University who did a thesis here and in this we actually we combine the risk or analysis and the MOTA of different stakeholders including construction specialists who are most from investor and the authority and the rest is how they perceive about the green infrastructure apart their developments. So you see there because of the most likely looking at the business group we try to commit the risk and in term of investment to support the investment. So what they mentioned about financial of the need initial is financial to invest in green blues, green infrastructure and other risk like in financial operational risk operational incentive or intangible risk. So we are looking at the difference incentive and risk considered from by different sectors. Yeah, and then also we have the motivation and abilities of different stakeholders from private sectors and local occupants which is residents and local authorities about them motivated or motivation and ability in term of investment on green blue infrastructures and you see there also by a diverse amongst different group from very supportive and also to oppose this green blue infrastructures. So it is mean in Vietnam or at least here in Ho Chi Minh city, the green blue are still quite controversies to different group of people. Yeah, so I'm trying to give you some example of our application of Mota in Vietnam and you see that can apply in different states of the project cycles and also and can be also in context and ecology to water infrastructures, urban flood. Ashman's and from the Mota, you see the discourse among stakeholders is by basic considering and on the prioritizing measure of in the source of long or long and here we apply by with others. Here we know that it can be with a livelihood framework or system services and this can give more of the context. Yeah, I think the last slide. Thank you very much for your, thank you. Thank you very much, Kwan. That was very interesting talk and I think it really shows the flexibility and the compatibility of Mota as a standalone tool by itself but also that you can integrate it with other tools to dependent on the situation that you're in. So we have some time for questions. So there's a question from Mike Aikist around Bayesian belief networks and that a Griffith University in Australia is working with Kanto University in Vietnam on a probability model based on farmer information on freshwater availability for shrimp success and the climate change. Have you looked at this model or have you looked at Bayesian belief network models as part of your experience? Um, John, sorry, can you say again? I am really not... Yeah, so it was just a question from Mike. He was asking about there's been an application of a Bayesian belief network modeling. Yeah, yeah, yeah, yeah. Oh, yeah, sorry, the question from Michael. Yeah, I actually, I don't know much about the Bayesian belief network. I expected this is a very interesting tools and I know some people are working on that, yeah. And I think some... I'm not sure if actually not only Kanto and Griffith but I think there are some other groups also working on the Bayesian belief network and they do a lot especially for understandings the farmers, the behaviors in the coastal areas of the Mekong Delta. So there are also some other framework have been developing there, including like agents based modelings to see the behaviors of the farmers. So there's another, I think also group from Queensland University also working on Bayesian belief network on mangrove preservation in the Mekong Delta too. I don't really get insight about their work yet but I think I know there is a thing. So thanks Mike, I'm not sure if I get your point. No, that's correct, and the university you are talking about is Griffith University from Australia, working on Bayesian belief network. And it's an interesting question between motor and Bayesian belief networks about also the information I think from motor can feed directly into Bayesian belief networks to strengthen the information that is being put in there. So I do see those also that is also a complementary that motor and Bayesian can also be complementary to each other, for sure. And I don't know whether Leon or anyone you have worked within that space but I've worked in Bayesian before and I see motor actually being able to produce a lot of really good quantitative information that could be used in Bayesian belief networks. So we've got another five minutes of questions for here. I posed a question on some of the groups of farmers had very high ability and very high motivation and I wanted to know, do you intend or do the authorities intend to work with those farmers so that they work with the other farmers that have less ability and less motivation? Is there an idea that you would use your strong farmers to work with the less enthusiastic or weaker farmers? Is there something around that as champions? Yeah, I think that that's a very good question. And I think this also something that we are now starts something like we develop something like a pilot in changing or transforming the livelihoods. So we need to identify who's the champions and we can work with them for example on the new livelihoods changing from this to another one. Then with this one, we can start with them and with them if we come up with some good results and then all the farmers will follow because the farmer are still very hesitate to change their livelihoods because changing practice, changing things is not easy. So yeah, but we very often in some areas we have the champions who have interest in new things and new market and new practice. Yeah, so yeah, especially now in the Mekong Delta we are trying to get more young people go back to the countryside. Yeah, and then with these people we hope very much that they will be the leading group. So in doing something new, for example, testing the new policy on agriculture practice. Yeah, so again, so if we have sometimes when we apply the MOTA framework we can see different groups. It can be from different sector but can be also within the sectors. So we identify who they are and we can start piloting with some who like the most followers or the most supporters groups to test the new policy, to test the new practice. And it's so evident to other groups that the way what we're doing here is good so that other people will follow. So of course if you take some more times in implementing the new policy but medicine will be more sustainable in terms of we don't have to change the whole livelihood. For example, in the water infrastructures project I mentioned, so when they build the sluice gate it means they change completely the water conditions. And many people there is they're not able to change their livelihoods because like they're already working with salar water now the fresh water they're not really familiar with the rice practice. They was with the shrimp and now they cannot do it. So yeah, so I think the champion... That's very interesting, Kwon and to also explore that the aspect of MOTO being able to identify champions and also innovators I think is to be able to test to pilot test new policy or new legislation. I think that's actually a very good insight into the application of what MOTO can help you to do. And it fits also with another technique called the fusion of innovation which I think is another interesting technique that can help within MOTO as well. Just quickly there was one last question from Leon and I thought it was a very interesting one about so the results that you have got from your different case studies just quickly how have they been received by the decision makers? Does it influence their planning? Is that have you found that the information you gather is influencing the decision maker to change their plans or to modify their plans as yet? Yeah, it's a pity because most likely our work is like a research project. So we are not really work together with the governments about that. So actually one of the dreams that fee like very much is that the MOTO can be used at one of the tunes to go together with others projects, procedures. So for example you have like as a tune required for example environmental impact assessment. So if we can include this and then we can go with the real projects and I think we can use that. But really affect to the policy is not easy. But we also join for example some last project with the work bank groups in terms also on agricultural transformation in the Mekong Delta. And one summer of fighting was introduced in the work bank report. So yeah, and I hope this was taken into account into the business making progress in the Mekong Delta. So actually this one is still under final consultations. So we work together with the Institute for Agriculture Policies in Hanoi in the final stage of a consultation. So I hope our fighting was there and was also presented to the government. So thank you, Kwan. That's very interesting. It will be interesting to follow up also to see to see that her influence their decision making. So thank you, Kwan. We will move on to our next talk, which is talk number three and a different moving on to also our topic number two. And it's around motor and similar methods. So Leon is going to present here and just some background on Leon is that so he works for at IG Delft and also TU Delft in the Netherlands. And his research and teaching focuses on policy analysis and evaluation of water management in multi-actor systems. And with his colleagues, he wrote a book on actor and strategy models which was published in 2018. And you will see from the presentations that have gone on from also from webinar one and webinar two that the group that is working around motor is very diverse from an engineering to natural science to social science scientists as well. And I think that shows the one of the strengths of motor that it really takes that interdisciplinary and transdisciplinary approach to finding out what is it that motivates and what are the abilities of people? So thank you very much, Leon. Yeah, we look forward to hearing this talk. Yeah, I was trying to unmute, but somehow it didn't work. So I stopped presenting, but I will start it up again. So bear with me. Yeah, and then I hope we are good to go like this. Okay, so thank you, John, for introducing me. With this talk, I would like to shed some light on the position of motor in the larger landscape of what I would call model-based actor analysis or actor and strategy models. Because also as we have already heard in today's webinar, there are a lot of similar methods and approaches out there. We have just now discussed Bayesian belief networks also applied in Vietnam, but there are in fact many other similar methods and models out there. And I think putting them on sort of a map, a mental map for ourselves, will help to see how these models and motor can benefit from each other, can complement each other and how this can perhaps also inspire some cross-fertilization to improve these models for both science and for planning. So I want to quickly go over the first few slides because they're mainly an introduction that I think may not be needed for this particular group. I think we all realize that beyond affordable, cheap, good technologies for water management, the human factor is also quite important to make sure that users accept these new technologies, but also to ensure that we have organizations and institutions in place to deliver these technologies and to maintain them during their lifetime. And even if we would have these institutions and organizations in place, even then the human factor or the organizational factor remains important because the way that organizations operationalize the policies and apply them to specific areas may have an important impact to the actual water that is being delivered to users or that is available for various societal values and ecosystems. So these two very short examples on these slides will help to illustrate that. I will not share the details, but if you are interested, the slides will be put on the website and the links are there with details on these two particular short cases as illustration. And that, of course, that importance of the human factor raises the question, can we then also model, quantify and even predict what happens around these human dimensions when we first started with MOTA, when Professor Phi first launched the ID and especially the idea of making it very quantitative. Many initial reactions were a bit skeptic because there is a widespread presumption that when it has to do with human behavior, quantification and prediction is not something that is really feasible or sensible. But I would like to turn it around if we can quantify and model all kinds of complex physical and social technical systems that we also don't fully understand to the lowest level of detail and the full complexity of all these linked models, then why not give a go also at modeling this human factor? Because in fact, we know now that also human behaviors are structured, they are not purely random. So there are things that condition human behavior, there are things that we know drives human behavior. So why not try capture those in models as well? And in fact, in planning and decision-making, we often refer to these cost-benefit analysis. But of course, that is nothing more than applying a very coarse economic model and an economic model is a model of society and of social processes into planning. So let's see, and if we can do a better job with the theoretical knowledge and the models that have been developed around this human factor before. Motor is definitely one of them and there are actually quite a few more. If we want to make this into a serious endeavor, I think it does help to have a conceptual map in which we can locate the things that we would want to model and that we would want to develop theories and models for. One thing, which is I think the thinking closest to many of the water experts and engineers in the water field is to think of a socio-technical water system, a system of interest, I would call it as more general policy analysis terminology. The system of reality that we are interested to solve problems for, to improve the management for and of course, that system is not a purely physical or technical system, but it will include social, institutional and economical components and the actors or stakeholders that are active in those systems of interest, I think are aptly called system agents. But of course, if we are interested in planning decision-making policy analysis governance, then we also need to consider another system, which is, you could give it various labels, I've called it a decision arena, following especially some work from Eleanor Ostrom and similar policy analysts and institutional theorists. And in that decision arena, there are another type of stakeholders active, which you could call strategic actors. They interact also with each other on how to make decisions and how to make policies to manage and influence that system of interest. And those are typically two different types of actors or stakeholders, if you want. And I think if we are thinking of quantitative models of stakeholders, of this human factor, it is really important that we distinguish between those two types of systems, because in a decision arena, we have fewer numbers of strategic stakeholders, more differences in interests and their abilities among them. So typically perhaps requiring different types of approaches from the system agents, like consumers, farmers, households, water users, that individually may not have a significant influence on the system if one farmer decides to change irrigation practices at the level of the river basin, we will not notice. But of course, if multiple farmers collectively start doing this then through emergence, we do see an influence, but that is an important difference requiring different types of methods. So far the MOTA methods that we have seen and developed focus on societal adaptability among farmers, among water users, among the private sector. And we have seen some early applications of implementability on the arrow between the decision arena and the system of interest, because even if a decision is made and the policy is decided, implementing that policy also requires, again, different types of stakeholders to become active and organize or at least configure themselves in what you could say is an implementation trajectory. There we have seen MOTA applications, but there are many other models around that we could consider using. Typically these models are developed more for certain application types than others, but as with all models, all these models can also be stretched and adapted for other users. The list on this slide is definitely not complete, but it will at least give you an idea of some of the models that we have. And what I would say are actor and strategy models are those models that not just assume some kind of bounded rationality, so not full rationality in the classic economic sense, but we do assume that stakeholders take their actions to ensure that values and things that matter to them are catered for or are taken into account. And more particularly we are interested because of resource dependence. What one stakeholder or one strategic actor does can influence the values and the objectives or achieving the goals of another actor. And that is something we could model and we could perhaps also model a bit further than what is currently contained in MOTA methods. So to just give you one example of the potential, one very simple example of the potential and that will be the last part of this short introduction, consider nature-based flood defenses. This is a case from the Netherlands, but in other countries you could think of similar structures where we have a dyke or a hard infrastructure type flood defense protecting some land behind it, agricultural land, urban areas. And in front of the dyke, there might be some nature-based vegetated area, a foreshore like a mud flat or a mangrove in other countries and other places of the world. And of course that has a flood protection function as well and we could also use it for that function as a multifunctional area. Of course, when you want to do that, it requires cooperation. It's not just the flood protection authority in charge of maintaining the dyke, but it then also will involve perhaps the nature authorities in charge of the area at the sea side of the dyke and the authorities and stakeholders active behind the dyke for the values that they care about. We can make a stakeholder map. We could even perhaps further quantify it with tools like MOTA, but we can also make a model to test some of the assumptions we have about how these kind of structures could be implemented in these stakeholder systems, in these governance systems. And in this particular project, we assumed within the project team and within the participating government authorities that if we want to test this new type of flood defense in reality and see how it works and how stakeholders negotiate over it, but also to learn about its impacts more, we need to go for economies of scale, so we need to go for a good chunk of land in front of a dyke where we could expect actually quite some benefits of interest to quite a few stakeholders as a good starting point and also to be able to observe these effects. We tested this first very simple assumption which was very essential to the project as you can imagine with a very, very simple actor model using game theory. And we just tested, is it better to go with a small foreshore or with a large foreshore as a pilot area in this project? And this very simple game theory model showed us that the additional assumption in the project team and with the water authority was actually not the smartest thing to do. And once you realize that, you can also see why that is, but of course to first prove your initial assumptions wrong, these models can help a big deal. I will not go into the technicalities, but the insight from this model was that if we have a larger foreshore there, then also much more is at stake. Not just for the water authority, but also for the nature organization that is in charge of, that owns this foreshore and expecting them to change their operations in a still uncertain situation might not be something they are readily willing to commit to if it is a large piece of their land and that makes it easier to start with smaller pieces of land. That makes perfect sense. And once you know what you will say, yes, of course, stupid, why didn't you think of that before? But let me assure you that the thinking throughout the project team, including the water authorities and municipalities was to first look for larger pilot areas rather than smaller. And this signals the dangers in doing that. Of course, you can think of other types of models like for instance, these social network analysis, I think would also make a good complement perhaps to MOTA, which could help you see what is the structure of your network, who connects different critical clusters. This is another type of project. I'll just want to show the illustration here. It was for metropolitan region around Amsterdam in the Netherlands, but also MOTA I think can be seen as a model because behind the quantification is this conceptual model of the types of variables that influence motivation, ability, opportunity and trigger factors. This is what was presented by Shipley earlier for Bangladesh. So also this I think is very useful to gain more insights into human behavior. So with this, I've presented what I wanted to share with you as sort of an introduction to open up thinking about other similar or compatible models to MOTA to benefit further developments in this area. For further reading, I think the MOTA manual is definitely the first point to start on MOTA, but there is also some further reading if you are interested to read a bit more about these other types of models in an earlier publication that we did and that we also use in some of our teaching in Delft. Okay, that was it. So over to questions and discussion. Thank you Leon, that was very interesting. And I think that's probably one of the biggest questions I get about MOTA being a new application from our perspective at our institute and around even within the Australian water management scene is why MOTA and why not other models? And I think that's a question that we have posed quite a bit and then also complementary models to that, MOTA strengthening one other application or another application strengthening that. And I have one question on that so within the stakeholder analysis and the context analysis component, what do you think is one of those complimentary applications or whether it's game theory or whatever that is really able to help lead from that step one into step two? I think that that would really, for instance, for us who are just starting off, that would be helpful to get some insight into within the context analysis component. What do you think is a complementary model within that? Well, so if I see the MOTA application, it starts always with this context analysis to basically set up your MOTA model and to decide on the variables and the kind of stakeholders you want to capture with MOTA. What I would do and what we actually also do in our teaching in Delft, both at IHE Delft and at TU Delft in the policy analysis program, we do proceed MOTA and that's also in the MOTA manual effect with a more general stakeholder analysis, just a scan of who are the stakeholders, why are they involved, what are their interests and can we perhaps even map different arenas of stakeholders or different subsystems around that? But to other particular methods that are or approaches that are very useful to complement MOTA with is to realize that we are not often not just interested in only these stakeholders, but we are interested in these stakeholders because we do think they influence a problem in our water system. So for that matter, I do think it is also useful to make a simple conceptual causal diagram of your social technical system. Just map it as you understand it as your initial context mapping tool and to consider playing a bit perhaps with the longer term and the shorter term, especially if MOTA is for strategic planning, then the short-term, long-term trade-off or tensions are very important and what is a very useful and also very, fairly simple tool is context scenarios and then play around a little bit with them. And together, these three methods, I think will inform a good design of a further MOTA study. And then what further kind of models or methods could be used to complement MOTA, I think that that goes in further steps of MOTA, step three or step four, step five and that would really depend on what you want to use MOTA for. I think one of the really strong points of MOTA, at least for me, is that it is quite close to the stakeholder analysis thinking that is so widespread, but it's just much more thorough about it. And I think what Kwon mentioned in his discussion and as a proposal or as an ID from Professor Fee to, I think it does lend itself really well to integrate with mainstream environmental impact assessment or social impact assessment practices. And I think those are really strong points for MOTA. Yeah, okay, thank you. That's a very nice answer and also very helpful for us who are moving into this context analysis stage so we can talk also about that more through the group. So we have a few minutes. I want to ask one other question was the diagram you put up about the dyke system and creating the wetland structures at the front of the protection area. Did the information that you came up with from MOTA, did it help to influence that decision making about large versus small? So did the authorities take that and say, okay, we will start small and not big? Well, that particular example was at the start of the project but that influenced the authorities to take these kinds of methods serious and to consider them. The researchers took their own pilots regardless of the authorities. But in a later stage of that project and partly because of these insights, the main researcher on this project, Stephanie Janssen, who is now working with Deltares, she used game theory structures to design an interactive workshop in which all the regional stakeholders participated and together identified good promising next piloting steps to take this approach further. And that was structured along the same variables that you would use for a game theory model yourself within an interactive format over two separate sessions in time. And out of that workshop indeed came some decisions including for how to approach these pilots that were taken up by the regional water authority as part of their plan moving forward with this particular stretch of dyke which they also have to strengthen under Dutch policy. Okay. But for reasons of time, I decided not to present that full story but eventually, so yes, it did help them actually. Yeah. Okay, that's great. And so Nora has one question before we move into Nora's talk. It's in the chat box and she says, thanks Leon, interesting to see these different active models. Have you been able to make an analysis already from changes in, for example, social networks and what induced these changes? So yeah, have you looked into that further? So the social network studies I have been involved with mainly with graduate students or colleagues, we have not done these more dynamic social network analysis but really more the snapshots of the current situations which are useful of their own especially if you overlay perhaps different types of relations in the networks. These dynamic social network analysis are also being done and I think can also be very useful but there is a caveat there that it is very difficult to get comparable data from perhaps one or five or 10 years ago and now or perhaps even over time. Doing that would require a good design and data set that you were then also perhaps constrained to rather than designing your own survey questionnaires. And of course you can ask people about relations in the past but we know from social science research that those answers tend to be more biased and less accurate than other answers. What is also being done what I also see in other colleagues is to do in other institutes to combine social network analysis with more modeling studies. And then of course you can through your model make these pictures a bit more dynamic and test your modeling assumptions as to what would change over time. Yeah. Okay, thank you. So thank you for that. It was a very interesting talk and I think it's something that we will also take forward with motor into the future as our group is here exploring and through the encouragement of also Dr. Fionn exploring how these different models fit together a complementary, yeah, to the applications of motor. So I look forward to discussing that into the next phase of this collaboration. So Nora is the next talk. So this is our final talk and we're running very well to time. And so Nora is also going to talk about motor and similar methods and something that's called readiness levels. So Nora, she works at IIT Delft and VUB KU Luven University where IIT Delft is obviously in Delft Netherlands and then KU Luven is in Belgium. And her research and teaching focuses on participatory decision support for natural resources management and governance. She has a key interest in the code as on innovative yet implementable natural resource management systems considering different value systems. So this should be a very interesting talk. Thank you, Nora. Thank you, John for this introduction. And also thank you to the other speakers. I saw a lot of elements that I can build up on. So as Leon, I'm going to talk about similar methods to motor and the research that I'm presenting is coming from a Horizon 2020 project called NIAID that was looking at the insurance value of nature-based solutions. And so the challenge that we were having in this project was to assess and demonstrate that the insurance but also the assurance value of ecosystems. So the assumption, as you know, as also was shown by Leon and earlier by Juan, is that ecosystems can contribute to mitigate extreme water risks and at the same time increase the resilience of a society in a context of climate change. And so what we want to do is to move from a situation now where insurance or risk reduction is a lot about the NIAID and conventional infrastructure and move to insurance and assurance schemes where nature or ecosystems have an active role. And so the idea that we had was we have to show this assurance value of ecosystems so the potential of the nature-based solutions to reduce damage costs related to floods and droughts, which was a focus, and then also to provide associates for benefits as part of a natural assurance scheme. We did that looking at the number of demos across Europe. So you see on the map here, the demos were rather heterogeneous in terms of the type of hazard that was most important to them, droughts, floods, kind of very, very high intensity mountainous floods or more slow riverine floods. The scales of the demos were different. Also, we had some urban demos, some rural demos. And then of course, all of them were located in a very different social, political and economic context. So how do we aim to move from just assessing the performance of nature-based solutions to demonstrating really that there is assurance value to implementation? We were keeping the end in mind. So what we were expected to do from the rise in 2020 program so from the research program was to move up the technology readiness level of the nature-based solution. And that was also really the formulation that they use. So technology readiness level was developed by NASA in the 80s, and it's kind of a indicator-based scale that shows the maturity of a certain technology. And so here we said, okay, the nature-based solution is an innovative technology to deal with water-related risks. And we have to bring that up the scale. We had to do that for different types of nature-based solutions in different contexts, working with different disciplines. And so what we wanted to do is to define a roadmap to have an evidence-based discussion on the co-benefits of nature-based solution or even to go a step further and to discuss action plans on how to implement this nature-based solution or the final step what we really were after was actually come to a full implementation of these nature-based solutions in some of our demos. And so we had to develop strategies to overcome implementation barriers and come to sort of a nice approach to nature-based solution and natural assurance scheme planning and implementation. So where we wanted to go with that, we wanted to have these nature-based solutions implemented. But then we also realized, this is based on a very extensively to show review many projects on nature-based solutions that have happened already. The kind of barriers that exist to get these things implemented. And we identified four main categories. So you have your institutional, your regulatory barriers. You have obviously the funding and financing barriers. You have knowledge and acceptance barriers and then also the absence of a clear evaluation of the performance itself. So in implementing the nature-based solution, what we have to actually do is to overcome those barriers and to manage uncertainties to move to higher readiness levels. And doing so is really depending on engaging in an interdisciplinary and transdisciplinary dialogue. And what we suggested as a way to have such a dialogue was the participatory planning framework. Now, this may look very obvious also to this group, but in our project where we were coming from a lot of different disciplines, a lot of people coming from the disaster risk reduction world from all sorts of other approaches to risk, this forward-looking idea of planning was something a bit new to them. So we proposed this because we wanted to have a shift from an exposed response where you just come in after disasters happened to an ex-anti-mitigation adaptation approach. We wanted it to be an adaptive framework because there's a lot of uncertainties inherent to all sorts of interventions, but particularly also with nature-based solutions being a living solution where we don't yet have a lot of evidence of longer-term performance. We needed a flexible approach to handle those uncertainties and also from the idea that it is more important to manage uncertainties than to reduce them. And finally, the participatory aspect of course is related to the importance of having the stakeholder engagement from a vision of the project through its implementation. And so what we wanted to do is to integrate a nature-based solution into a participatory-adaptive planning approach, just as kind of a short view of what that approach is about, probably known to many of you, but in this planning approach, you kind of define different stages in which you gradually move from an inception of, in this case, a nature-based solution, the context or situation analysis through building strategies around potential interventions and a selection of, let's say, the most performant, the most desirable intervention, and then moving into the operational planning of how you're going to build, operate such a solution, a nature-based solution, with the final step, the implementation. Now, what is important to see here is the stakeholder engagement that goes across all of these steps and also the feedback loops of the adaptive pathways that you can have, where through the monitoring and evaluation of the performance, you can actually make changes in the way, for example, your nature-based solution is operated or is managed. So what did we learn from the demo side? So we looked actually at our demo side kind of after they were being implemented or after it was designed, and we looked at the implementation of an existing nature-based solution through this participatory adaptive planning lens. And what we did was to, for a few of the selected demo sites because they were all at different stages of implementation, for the ones that were further advanced, we looked back and to see, okay, how can we now understand the implementation? So we were recreating a storyline of how nature-based solutions move from inception to implementation. Now, important to know is that this is kind of an artificial storyline that is beautifully going in a linear way. Of course, we know that reality is much more messy and what is happening in context analysis action plan is moving a bit across each other and it's also kind of a circular process. So the analysis method that we used is to look throughout that implementation process at what kind of barriers and uncertainties appeared and how they were overcome. And then we looked at different types of uncertainty, so unpredictability, incomplete knowledge, but also the presence of multiple knowledge frames and a maturity. And we were identifying what were the key success factors in overcoming those barriers or managing that uncertainty. We looked at how information was used and by whom and how elements of implementation plan and the business models were already prepared in early stages of planning. And this is then a view of what such an exhaust analysis looks like. So this is for the one case nature-based solution that we analyzed for the Rotterdam and Urban Rainwater Harvesting Project with some reuse for it. And you can see here on the left side these different steps of the planning process. And for each of them, we identified in yellow what were key success factors, red but for the barriers, green but for drivers and purple what are enabling activities or agents of change. And I said before, so you can see that this is not a nicely linear process. We have already some action planning activities at the very start in 2015. But this analysis really helped us to identify all the elements and see where barriers were overcome and what was the key success factor for that. So we did this for different demos that were at different levels of technology readiness. We discussed experiences with stakeholders. And from those discussions, the learning that we had was that if we want to move up with readiness, which was the initial request from the European Commission in the research proposal, you have to come to demos that have a high technology readiness level. And what we realized when doing this exposed analysis, and of course it's not a fantastically super original realization, but we managed to demonstrate rather nicely how that readiness is really a combination of knowledge, governance, and investment readiness. And so we were communicating, as of then, not in terms of technology readiness alone, but also of investment readiness and institutional readiness. I have done quite some work on defining what is underlying those readiness levels. And I think here, in relation to MOLTA, what is interesting is to think about the levels of readiness, not the levels of motivation or the levels of ability that you want to achieve and how you can build it up and how you can also identify where you are and where you want to go. So one thing that we did was that to say, OK, in this process, this very adaptive process, actually this is a process in which we are building up those different types of readiness. So that thing with technology readiness level, that goes from the inception, context analysis, and the strategy building actually is when you already have to be at a very high technology readiness. The investment readiness is something that is not just happening in the action planning, it's something that starts very early on as well in the inception, the discussion with stakeholders. I don't have the detail here in this presentation, but we developed a modified business canvas for natural assurance keys. And we showed how you build up that business canvas through that planning process. And likewise, with institutional readiness and the six components of that institutional readiness, we showed how this is happening through these different stages in the participatory adaptive planning process. Then I want to add with some reflections on the readiness levels and more on the readiness level. So these readiness levels here is the interest for us and in the context of European research is to really connect to a language that is used also already by policy makers. So if you think about the science policy interface, we need concepts that speak to different audiences. And here, the investment readiness and institutional readiness and particularly the underlying information of what consists of that and how you can build it up was something very useful. What was also shown is the importance of the process and the use of that participatory analytical planning as an analytical framework to analyze what is happening. And then to understand advances in readiness, we also developed a self-check. So self-assessment, some questions where demonstration sites can identify at the start what is their existing readiness level and then combine it with offering a portfolio of methods and tools that will help to increase the readiness. And particularly in our discussions or in our demo sites, what we were seeing is that whereas most of the resources were still going and trying to increase at technology readiness level related to the knowledge and the physical modeling and all sorts of modeling to show what potential impact the nature-based solution was having, the real barriers were really situated in this investment and institutional readiness. So if you make that really obvious from the start, then of course it can also help to relocate resources to that and really foster the implementation. I also put a few references out here for further reading if you're interested. This is all very new work. We're just still kind of writing it up. So people are still free to email me if you want to have more information on this. Thank you. Thank you very much, Noor. That was very interesting looking at, yeah, looking at nature-based solutions and then participatory planning approaches for that. And I think it was an exceptionally ambitious exercise to take on that many countries, cultures, areas, size, and everything. And I wanted to ask you, actually, with that question was, so when you were performing the country context analysis for all the, at least the context analysis for your different areas that you were working in, the different pilots, did it make a big difference to how you went forward with your participatory planning approach? At the start, no, let's say. I think that this kind of realization of having to look at the different types of readiness was something that only grew more or less in year at the end of year two of the project. And so in most of the demos, the focus was really still on doing all kinds of flood modeling and landscape modeling and those kind of things. But it is true that when, through the stakeholder interactions and all these feedback that was coming, what happened in a number of demos is that the concrete effect was that, apart from having these purely interventions related to the biophysical world, there were a number of other interventions identified that were targeting the governance that were targeting the investments. And somehow, to me, it was a bit strange to see that still now, still. And I know, John, that we have had many conversations about that. But making this a bit more obvious that it's really not only about the technology readiness level was very important even for the researchers within the project, or maybe particularly for the researchers within the project. And so then, if we are working in this multidisciplinary team, it was also good to kind of balance out what had to be the focus of each of us. And I think that leads well into Professor Fee's question where he says, why did you not include a social readiness at the beginning of the, as you worked into those readiness levels? And it probably had something to do that technology was the focus, not the social aspects, I guess. Yeah, so I mean, the technology readiness level came into the project because this is really how the call is written. So you need to bring up the technology readiness levels off your nature-based solution. And then we saw that that concept is really not sufficient. It's not about the technology readiness level, or at least not alone. But yeah, social readiness and market readiness, so for us, the market readiness is part of the investment readiness. So it's both the creation of the market, but also the connection of the different institutions and individuals to that market. And the social readiness, I think, there's a lot to say still. I mean, we focused on institutional readiness. And when I was also listening to the presentations of MOTA, that focus on the individual actor is something that we have not been looking at. And so this is kind of a more kind of aggregated understanding of how that readiness level is built up. And I think it's interesting to go more into that, to see how you've created it. Yeah. And before I come to your question, Leon, I will actually go to Shipley's question because it leads on from this, where he says, do you think the societal adoptability of MOTA can reflect social readiness? So if you used MOTA to come up with that social readiness, would that, yeah, do you think that would help or that would be able to be integrated into what you were doing? Yeah, so I was thinking about that too. And I see that this readiness level are focusing quite a bit on the ability component and then that the motivation component is not looked at at an individual level. So I'm not sure about the adoptability jump. And this is the question of Shipley. So maybe, Shipley, can you understand how you see that yourself? Sorry, Nora, it dropped out just as you were speaking, that last part. So yeah, I'm not sure about the social adoptability. But I wanted to hear from Shipley, maybe, how he sees that himself. So we still have some time. So Shipley, do you actually want to ask that question yourself? You should be able to unmute your own microphone. Yeah, thank you, Nora. Yeah, it was really a nice presentation. And yes, the readiness level, that attitude are really interesting. And I think, like, yeah, we can somehow integrate MOTA in that case and the social adoptability, though it is the, this is from the perspective of the local community. But maybe, yeah, maybe I'm not sure it's possible to integrate. And maybe we can come up with something with social readiness as well. But yeah, there is one difference. When we ask MOTA, we ask it from the perspective of community. But when it's the readiness, it's from the different perspective. But that would be interesting if you can have a picture of that. Yeah, exactly. So in our team, we had one of our partners doing quite some work on risk perception and social network analysis like that and how that risk perception was evolving through exchange with the knowledge, not through exchange, through discussing performance of nature-based solution or having maybe also more security on the investment or the liability discussions. So we have, it is somehow part of the institutional and investment readiness, but we haven't really put our finger on it yet on how that would look like. So this is indeed, I think, something interesting to explore. Thanks, Nora. So I think that's actually, and that's, yeah, Shibley, that's something we can take forward also in as part of this group to look into each other's projects and to answer some of those challenges that we have. And so Leon had a question about, could you say something about how the actors or the actors stakeholders are part of the institutional readiness assessment? Yeah, so in this, there is kind of a self-check list of questions that is asking about this component of the institutional readiness. And here we did ask that through a different stakeholder workshop. So it was kind of a discussion on whether they thought that the organizations were equipped, whether the, we had quite some conversations actually about the liabilities around the performance of nature-based solution. And that was one of the main things that wasn't really understood. But also, even in the Rotterdam case, where you would think that maybe nature-based solutions are a bit more adopted already, one of these major issues really was the within one organization, the municipality, the fragmentation of the responsibility for the different departments. And so people not wanting to take the responsibility of an nature-based solution because it just wasn't sure what kind of quality was going to come out of there. And also not having the good standards established for that. So this assessment of the readiness was something that we did through the discussion with the stakeholders. Yeah, OK. And I think that leads, well, also into Professor Fee's question, where he said, for instance, in Kwan study, the green solutions, so the nature-based solutions that you've been presenting that have been introduced into developed countries are not socially ready for Vietnam. And I think that's a challenge within many of the Western performance-based projects that we're utilizing within our own countries and then thinking that they are applicable in other countries, that transferability that is a real challenge. And without an understanding of the social aspects of the country that you're in, it can lead to huge challenges within those performance-based projects. And I think we've been doing that basically since World War II, haven't we? So yeah. And then so there was also an interesting question from Liliana from, I'm guessing she's from somewhere in Latin America. And she asked the question, is there any experience of motor applications in Argentina or the rest of Latin America? I don't know the answer to that. So Leon, or Ya, I don't think so. I don't think we have that experience yet in South America. Yeah, and it's something that we were discussing earlier, that we would really like to get some experience there. Certainly. Yeah. Yeah. So it's open to suggestions, especially at IG Delft, where you have, and at IG Delft where you have good numbers of American students come through in Africa as well, as we spoke about earlier in our discussions. So I want to ask you one very quick question, Nora, before we wrap up and end with a couple of minutes to go about, so utilizing or using participatory-based approaches seems like a logical thing to do. But sometimes when you bring government actors together and society actors together, and if you look at water management in Australia, for instance, it's very much the case. It leads to a massive amount of conflict. So I'm wondering before you start the process of bringing government and society together, how do you propose that you know your situation before you enter? Like what would be a first step if you were charged for that? Yeah, I think Leon also mentioned before that the stakeholder analysis that you would have to do before going into engaging the stakeholders. And then there's all sorts of methods and tools to also analyze the relationship between them. And one of them is focusing on the degree of conflict that exists between those stakeholders. So in my experience, if this is the case, when there is really existing conflicts or almost exploding conflicts, then, of course, you're not necessarily going to bring these people together in the same room. But you might want to start with a separate engagement and then find ways after that to see whether they can come together. But it needs also, for example, we've been working quite a bit with Saki on that also. You need your diplomacy skills to run such a process properly. Yeah. So I think that's also something that's misunderstood within technical-based projects is often you could have engineers or natural scientists such as myself trying to run those processes with any real understanding of how to approach social situations well. And that can often lead to conflict increasing within your projects because you're bringing together different actors that are not ready to be brought together. So that's been a huge challenge within many technical-based projects that I've worked with. So we're right on the time, the finishing time. So thank you very much to the speakers and also thank you very much for your participants and the questions. So as I said, there will be a third seminar. Sorry. And that will be we will send out to all the registered participants, we will send out a reminder of that. And also the abstracts and who will give those talks is part of is all together on that on the website. And a part of that third seminar is also very much related to where do we go from here? So there's interested parties within the participants that have been part of this. How would they see themselves being involved? Or what are some suggestions? And then because we really we intend to keep this group going forward to build the application of motor both within our projects, but also within other projects around the world. So thank you very much. Any questions can be posed on the website. And we will do our best to answer them. And I think, yeah, I think those questions will probably come to you as a contact person. And already looking forward to, yeah, to the third seminar. So thank you very much. And good night, afternoon, and possibly morning, depending on where you are living. See you, everyone. Bye. Bye. Bye. Thank you very much.