Recipes from the Mashup Cookbook

Cookbooks are a tradition in software and mashups are all the rage today, so I couldn’t resist starting a mashup cookbook. But, before I show some of my recipes and what I have in the oven, I thought I’d explain the role mashups and social production play in solving a problem too hard and too expensive to solve under conditions controlled by corporations or the government.

An issue we face today is producing a sufficiently complex mirrror world to match the needs of an information society. By complex I mean a mirror world that nicely reflects the world in which we live. Consider the problem of ontology which has perplexed even the smartest of us since as far back as history can tell. Our need to understand the world causes us to attribute order to our experience. Whether it’s through Alexander’s Nature of Order, or Peirce’s Order of Nature, it’s our nature to understand the world through order. We use ontologies to represent that order. More times than not, we forget that the order through which we perceive the world diverges from our experience. When we forget this we mistake the order through which we understand the world for the world itself. We confuse mechanism with organism and models with the world. Alan Watts says it best in this 1969 lecture to a group of engineers from IBM. Tim Berners-Lee says Fractal Web, Fractal Society. Today, the mirror world distorts the world in which we live in some fundamental ways that mashups and social production are changing. Where corporations and governments typically provide large applications or large data sets with historical data, mashups combine information already available in unique ways. And social producers are autonomous resources that complement the capabilities of controlled resources. By complementing controlled resources with social producers, the mirror world better reflects the world in which we live by including otherwise missing resources from an information society as a whole.

Before I get out the ingredients and check the oven, I want to offer a few words of caution about some assumptions underlying power laws, crowdsourcing and long tail approaches in vogue today. Social producers will have to get quite a bit smarter than they are today so that coordination costs of social production do not exceed coordination costs under control. The cost of social producers is not free and the cost of low-knowledge producers in an information society is high because the information resources they produce have higher coordination costs. And the maintenance of these resources represents an opportunity cost. As I have previously written in Information Sharing and Tomorrow’s Knowledge Workers, knowledge transfer from the academic research community is essential in an information society. The assumption that the smart folks sit by themselves over at places like DARPA and universities and that technology is transferred to passive consumers with limited ability to understand the hard stuff doesn’t foster the growth of knowledge workers in an information society. An information society implies both technology transfer and knowledge transfer. Today, slow knowledge transfer distorts the mirror world and there’s no indication that social producers have an understanding of the hard problems of information sharing. Only a handful of experts have demonstrated they truly understand the hard problems. For many of the hard problems the solutions are even harder. And where knowledge is a differentiator, there’s no indication that autonomy and altruism as values that drive production will outweigh the innate selfishness in our genes. There’s plenty more to explore here, but that’s enough for now.

Anyway, back to the cookbook and mashups. Mashups are the right scale for social production to make a difference in the short term. So what makes a mashup yummy? My stomach’s growling for three ingredients: 1) Combining information resources that reveal information that is not otherwise available and that allow for more informed decision making. This can be at the level of indication, determination or inference. Note that John W. Tukey introduced this important distinction in his work on Exploratory Data Analysis and this work has been nicely extended by Edward Tufte. 2) Meaning, or semantics of various flavors. The semantics of truth as cooked up by Alfred Tarski in his Semantic Conception of Truth which serves as the bouillon of RDF model theory. Gently fold in some of Quine’s Two Dogmas of Empiricism and just a dash of Peirce’s On a New List of Categories. 3) Fresh ingredients. Only the most current information makes for suitable ingredients in a tasty mashup. Feeds and Tweets are all the rage these days and at the quantities these ingredients are served (20 items) on the Internet, these quantities are just right for where they”ll taste best in the recipe. So, what does Rick have in the oven? Here’s a screen shot of the data visualization.

data visualization screen shot

Cookbooks don’t seem to fit well in the blog format. Hopefully the screenshot should wet your appetite enough to check out the cookbook which you can find here.

Bon Appetit !