A Markov Model of the 4500-year-old Indus Script

Although no historical information exists about the Indus civilization (fl. c. 2600-1900 BC), archaeologists have uncovered about 3800 short samples of a script that was used throughout the civilization. The script remains undeciphered, despite a large number of attempts and claimed decipherments over the past 80 years. Here, we propose the use of probabilistic models to analyze the structure of the Indus script. The goal is to reveal, through probabilistic analysis, syntactic patterns that could point the way to eventual decipherment. We illustrate the approach using a simple Markov chain model to capture sequential dependencies between signs in the Indus script. The trained model allows new sample texts to be generated, revealing recurring patterns of signs that could potentially form functional sub-units of an underlying language. The model also provides a quantitative way of testing whether a particular string belongs to the putative language as captured by the Markov model. Applying this test to Indus seals found in Mesopotamia and other sites in West Asia reveals that the script may have been used to express different content in these regions. Finally, we show how missing, ambiguous, or unreadable signs on damaged objects can be filled in with most likely predictions from the model. Taken together, our results indicate that the Indus inscriptions exhibit sequential structure and regularities that are suggestive of a linguistic rather than a nonlinguistic writing system.