History of the Khipu Field Guide

Started by Ashok Khosla in the isolating tedium of Covid-19, in 2020, the Khipu Field Guide has allowed scholars the opportunity to take on a big, hairy audacious goal - khipu decipherment at scale. After the first set of drawings were made, in late 2020, khipu scholar and historian Manuel Medrano joined me while he was finishing his first stint at Harvard and the real decipherment work commenced!

Five years of teamwork have resulted in the following goals:

Project Phases

Phase 1 - Reading and Writing (Completed)

In phase 1 a basic understanding of khipu was achieved. This involves five steps.

  1. Yak Shaving - All data science projects start by yak-shaving, the affectionate name for the process of cleaning data, checking for integrity, etc. This project was no different - Sadly, integrity checks led to a loss of one-sixth of the khipus from the database. After removing the khipus that failed integrity checks, the Harvard Khipu Database SQL tables were transformed into CSV spreadsheets capable of being viewed in a variety of applications. The current database, now the largest and most accurate khipu database in the world, has taken over 5 years to build.

  2. Exploratory Data Analysis - What kinds of knots are there, how are knots, cords, cord colors and groups distributed. What are likely spreadsheet khipus, and what are possibly something else such as a narrative khipu? What things do we want to emphasize in rendering?

  3. Class Building - Simple Python functions and the Python Pandas Dataframe library will not easily provide the type of functionality and interface we need to draw khipus and do more sophisticated data analysis. A Python class object has been built built for each Khipu component, knot, cord, cord color, cord group, primary cord etc. This class library then supports the rendering of Khipu and of more tailored types of data inquiry and output.

  4. Khipu Rendering - Many khipu scholars regard khipu as a “tactile medium” (think of khipu reading as a kind of braille for example). Understanding them from CSV tables, is the farthest thing from tactile. Rendering is needed. Producing the code to satisfactorily render khipu has taken many, many months. As the Russians say, “It’s not a miracle the bear dances well. It’s a miracle the bear dances at all.”

Phase 2 - Reproduction of Existing Studies (Completed)

Phase 2 of this project was an exploration of existing work in khipu analysis. In this phase, studies of existing khipu were identified, reproduced, and occasionally extended:

This journey will provide the smorgasboard of analyses types and analytical tools needed to do more decipherment.

Phase 3 - Using Data Science and Natural Language Processing (NLP) Techniques (Completed)

In Phase 3 modern Data Science and NLP techniques were applied to Khipu.

The great bird educator and ornithologist, Roger Tory Peterson, produced the first bird “field-guide” of the modern age. Rather than simply showing a picture of a duck, Peterson had arrows that pointed out key features of a particular duck to look for in identification. The increased curve (the 2nd derivative in mathematical terms) of a bill of an avocet allowed you to identify it as female or male; the presence of a white rump patch allowed you to confirm that the raptor was in fact a northern harrier, etc. Now known as Fieldmarks, these key identification traits were be the outcome of Phase 3.

What types of Fieldmarks will we look for? As examples, we can examine verso/recto cord attachments, or the presence of Z vs S knots (i.e. Urton’s studies), or we could look for color and patterns (i.e. Sabine Hyland’s work), or cord distribution and summation patterns (i.e. Marcia Ascher’s detailed studies). Whenever we see something intriguing (as in it stands out), and at a higher level than a simple knot or cord characteristic, it will be noted as a potential Fieldmark. The goal of Phase 3 then, is to finish with a set of Fieldmarks that allow us to categorize khipu by “Family” - hence the name of this site - the Khipu Field Guide.

Phase 4 - Using Fieldmarks to Decipher Khipu (Ongoing)

Some of the decipherment work described here has been recently published by co-researcher Manuel Medrano and Ashok Khosla. Their article “How Can Data Science Contribute to Understanding the Khipu Code?”, in the professional archaeological journal Latin American Antiquity, presents our unearthing of common decipherment patterns throughout this site’s khipus. Many of those patterns are revealed in the Notebook. More exciting pattern breakthroughs are on their way to being presented!

There is an old joke. Stupid scientist does an experiment with a frog.
    Jump Froggy! he says. Frog jumps. Stupid scientist cuts off one leg.
    Jump Froggy! Jump he says. Frog jumps. Another leg. Another jump.

    Finally he cuts off the last leg.
    Jump Froggy! Jump! Nothing happens. He yells louder
    JUMP FROGGY!!!! JUMP!!!! Nothing happens.

He writes in his lab notebook, “After cutting off fourth leg, frog became deaf.”

So it is with khipu analysis. The decoding of unknown “languages” is fraught with stupid science. The goal is to use modern data science to tease out more information about khipus. Like Pygmalion, we want khipus to speak. However, I suspect, at the end of the day we will be ecstatic if we simply get them to mumble, squeak, or even make the sound of a punctured balloon.