 So, thank you all for coming. Thank you all for joining. I'm sorry I could not be there physically in the Davis office. Airline cancellations prevented me and the earliest I could get there was tonight sort of a thing. I didn't make sense. I am professor emeritus of information and decision sciences of the University of Illinois at Chicago, which essentially means I'm a retired professor but I still have facilities, the library and other facilities that I could use. And I'm also the director of the Ramaya Public Policy Center. Now, the thesis that I'm going to present in the next few minutes is that before we address a policy problem, we need to frame it. And that I call making the elephant visible. Then I borrowed from an old model of decision making Herbert Simon postulated intelligence design and choice. And what we're talking about is using the ontology to discover the gaps in research policy and practice and between research policy and practice that you can call intelligence. Design is in terms of designing the path phase for using the ontology to bridge the gaps in research policy and practice and between research policy and practice. And the last one is to choice of determining the pathways using the ontology to bridge the gaps in research and to bridge the gaps between research policy and practice. And the last one is about making the elephant dance and playing on words there. I have not seen an elephant dance either. But how can we address the problems of policy systematically systemically and in a structured way be to formalize it and I'm not good too much into the theory I'd be glad to talk a lot more about the theoretical aspects of it. We call an ontology the structure natural natural language representation that helps articulate and make visible the combinatorial complex complexity of a wicked problem comprehensively concisely and clearly. Now, what we also can do is. It can explicitly specify the conceptualization of a problem is gained the definition that goes back to Gruber. It can systematize the description of a complex problem, and it can encapsulate the core logic of the problem. Now coin goes a little further from it and he talks about as a theory of the problem. And then to the shaker and Josephson talks about it as understanding the discourse about the problem, there are various ways of looking at the ontology and and also another way of looking at this as a systematic and systemic frame boring from creed. Now, smart set this is very popular, and everybody wants to talk about smart city one of the things that we did was, we said what exactly is meant by a smart city. So what we did was to conceptualize what a smart city could be. And this is the ontology of a smart city that we constructed and we have a couple of papers based on it. Now, when you talk of the smart city one could break it up into the smart part and the city part. And we could think of the city part as having two dimensions. One is the cities the stakeholders and the outcome. When we talk of the outcome, we can think of the sustainability of the city quality of life of the city equity in the city livability of the city and resilience of the city. Similarly, in terms of the stakeholders one could think of the citizens the professionals communities institutions businesses governments politicians and educators. Now what we did was two things. One is we had to validate this with so we did present in a couple of places and we didn't get too much pushback on this in fact people complimented on these on the conceptualization. And what we also did was to take all the definitions that have been talked about smart cities in the research. So these are the parts of the two papers, a unified definition of a smart city and ontological review of smart city research that we have out there. Now, if you were to agree that this more or less encapsulate the logic of a smart city, what we did was to take all the definitions, and you say all pretty much all the definitions that were available to us, the scope of search, and decided to map it on to the ontology and see what is emphasized and what is not emphasized. So the numbers at the top indicate, for example, there were 316 articles that talk about the structural aspects of it. And of it, most of them talked about the systems and less about infrastructure, very few talked about the processes in the personnel that are required and the services provided. Similarly, in terms of the functions, about 38 talked about the functions, very small number of them. They talked to basically about sensing. Our distinct was that most of them talked about simply collecting data by putting sensors, very little about monitoring processing translating and communicating. Similarly, in the semiotics, the dominant focus is on data, not on translation to information and knowledge. Similarly, focuses on technological and infrastructural issues, not on the cultural and other types of issues. Interestingly, an outcome will notice that although a lot of this literature talks about smart cities, they really don't focus as much on the city as on the smartness. Let's talk about the stakeholders and outcomes, and within that the sustainability of the city and the citizens. In a sense, this is what we mean by making the elephant visible and also the fragmented view of it. Although there's a lot of literature on smart city, I think we looked at 330 papers or something like this. There is the focus is very narrow and selective. It is not comprehensive. And as a next step, what we did was to do a culture, the astral analysis of the teams, and what we find is the dominant cluster is simply focusing on systems. And the secondary cluster is focusing on infrastructure and technology. And what you see from the diagram is that there is a large number of aspects of the smart cities that are completely blank that are not covered in any of these definitions. So this is what we make mean by again making it visible and then one can then go on to designing what we want to do and I'll give you a couple of things that we did. One was, in this context, India has a program for smart cities, and we took one of the smart cities proposals, the TUM course, and then we tried to map what they talk about. Now I talked earlier about the gap in research and practice, you'll notice that the research emphasis is quite different from the practice emphasis. Now, we had to change the taxonomy a little bit because in this case, in terms of the focus, although we talked about cultural economic demographic, they were much more specific in terms of land use planning, public transportation, feeder systems and so on and so forth. And then what we find is, comparing this with the earlier one, we can find the gaps between the research and practice. And the next one is we use a similar framework, we conducted a workshop, and this is one of the groups, and we had one of the groups in which we said, okay, this is the data that we have. This is group two. Ideally, if you were to design one, what would your priorities be, and this is one way of eliciting the stakeholder priorities in terms of the smart city design. So with that, I'll wind up and I'll open it up for discussion.