Can Web3 survive without cognitive modeling?

talking i'm ready okay hope that this is working well and i guess this is where we can begin the conversation this is a twitter spaces on september 14 2022 it is called can web 3 survive without cognitive modeling and i know there's something they say about headlines that end in a question mark something like always there are yes answers or no answers or in this conversation we're going to be talking about web 3 and about cognitive modeling and it will be less than an hour long hopefully will serve as a nice introduction to those who have little or some familiarity with web 3 and cognitive modeling alone or together but it's also meant to be kind of a broad-ranging discussion and hopefully we'll have some other voices come up and add in some thoughts and questions let's start with just a description of web 3. surely elsewhere you'll be able to find audio and other kinds of content describing web 3. so i'll just describe what's relevant about it as we talk about cognitive modeling web 3. web 3 is an ongoing movement that is applying blockchain and other distributed technologies to a wide-ranging areas and we can think of web 3 as creating critical infrastructure of the public goods type or not necessarily public goods in a wide range of areas so in finance there's decentralized finance or defy and defy requires the engineering of token schemes which is sometimes called token engineering or tokenomics such that those token-based systems are able to be designed effectively and operate effectively and there's a lot of other areas outside of the financial that are important for web 3. for example community governance and meta governance like what people may have heard about dows decentralized autonomous organizations as well as all other kinds of organizations and of particular interest for our institute and for many others is decentralized science or design and in many ways dsci will succeed if it can have a balanced and coherent approach to tackle all the challenges that face the research and education stack ranging from the who what why where when etc our question that we'll return to is how can any of these areas finance community research science and education how can any of these areas be successfully navigated or designed for without taking agents into account so this is very related to agent-based modeling and we're going to explore how there are several approaches to modeling these complex systems and we're going to suggest that complementing top-down structural modeling of these systems there's a really important role for agentic or what we can consider cognitive modeling of the actors in these systems themselves and we believe that without this kind of like a bottom-up individual driven account taken conceptually into the design process but also more formally into the specification and evaluation of these critical infrastructure systems that there are going to be a lot of blind spots and a lot of failure modes that just simply will not be addressed by the structural analysis alone blue want to add on that or we can continue talking about this yes sir i can say a couple things so i think um agent-based modeling is a super useful approach when you are getting into systems that that are multi-agent that like are maybe predictive of any kind of outcome so like if this then that like how many agents does it take to defect um for a system to fail like you know if if um everybody unloads their nfts all at once then the system collapses all right so it's these kinds of things and and what is the the cognitive uh underpinnings what are the cognitive underpinnings of the decision making in an agent because everybody's influenced by a variety of factors uh personal and environmental so agents can start off with like more of a of a different disposition like than others which is how we are in the real world and um you know can have different reactions to environmental cues um and i think that that's all i'll say about that great…