Fitting an Elephant and Making it Wiggle


The quote, “With four parameters I can fit an elephant, and with five I can make him wiggle his trunk,” attributed to mathematician John von Neumann, holds a profound message about the power of mathematics and modeling complex systems. This intriguing statement highlights the ability of a few well-chosen variables to accurately represent and manipulate even the most intricate phenomena. In this article, we will delve into the origins of this quote, its meaning, and its implications in the world of mathematics and beyond.

Origins of the Quote

The quote is often associated with John von Neumann, a renowned mathematician, and polymath, who made significant contributions to various fields, including quantum mechanics, computer science, and economics. However, there is some uncertainty about its exact origins, and it is possible that the quote might have been paraphrased or attributed to him over time.

The Meaning Behind the Quote

At its core, the quote illustrates the concept of parsimony or Occam’s razor, which suggests that simpler explanations or models should be preferred over complex ones, all else being equal. In mathematical modeling, the goal is to develop a formula or set of equations that accurately describe a system’s behavior while using the fewest parameters possible.

Fitting an Elephant with Four Parameters

The first part of the quote, “With four parameters I can fit an elephant,” refers to the surprising fact that a simple mathematical equation with only four variables can be used to approximate the shape of an elephant with remarkable accuracy. This might seem counterintuitive, as one would expect the shape of an elephant to be far too complex to be represented by a mere four parameters. However, it showcases the power of creative and efficient mathematical modeling.

Making the Elephant Wiggle with Five Parameters

The second part of the quote, “and with five I can make him wiggle his trunk,” adds another layer of complexity to the model. By introducing just one additional parameter, the equation gains the ability to animate the elephant’s trunk, adding an extra dimension of realism and versatility to the representation.

The Implications in Mathematics and Beyond

Neumann’s quote resonates beyond the realm of mathematics, as it underscores the importance of simplicity and elegance in problem-solving and model development. In various scientific disciplines, including physics, biology, and engineering, researchers strive to find concise explanations that capture the essence of complex phenomena.

Furthermore, the quote reminds us of the incredible capabilities of mathematical modeling in various fields. From predicting natural phenomena to simulating economic systems, mathematics plays a crucial role in advancing our understanding of the world.

The approach of using a minimal number of parameters to model complex phenomena has found application in various fields. Let’s explore some more examples where this methodology proves effective:

  • Population Dynamics: In ecology and biology, understanding the dynamics of animal populations is essential for conservation efforts and management strategies. Simple models with just a few parameters can effectively predict population growth, decline, and interactions between species.
  • Epidemiology: Modeling the spread of infectious diseases is crucial in public health. Simple compartmental models like the SIR model (Susceptible, Infected, Recovered) use only a few parameters to simulate disease transmission within a population.
  • Image and Audio Compression: In signal processing, various compression algorithms employ a limited number of parameters to efficiently represent images and audio signals. JPEG for images and MP3 for audio are classic examples of such techniques.
  • Economic Forecasting: Economists often develop models to predict economic trends and fluctuations. Simple macroeconomic models with a minimal set of parameters can provide valuable insights into the behavior of national economies.
  • Weather Prediction: Meteorologists use numerical weather prediction models to forecast weather patterns. These models use a finite set of variables to represent atmospheric conditions and predict future weather scenarios.
  • Option Pricing: In finance, the Black-Scholes model is widely used to determine the price of financial options. The model incorporates just a few variables, such as the underlying asset’s price, time to expiration, and volatility.
  • Pharmacokinetics: Drug pharmacokinetics involves studying how drugs are absorbed, distributed, metabolized, and excreted by the body. Simple pharmacokinetic models use a few parameters to estimate drug concentration over time.
  • Engineering Design: Engineers often use simplified models with a limited number of variables to optimize designs for various systems, such as bridges, aircraft, or electronic circuits.
  • Neural Networks: In machine learning, certain architectures like perceptrons and simple feedforward neural networks have a small number of parameters and can effectively solve certain classification and regression tasks.
  • Game Theory: Analyzing strategic interactions among players in games can be achieved with relatively simple models containing just a few key parameters.

These examples demonstrate the versatility and power of simplicity in mathematical modeling. By using a small set of well-chosen parameters, scientists, researchers, and engineers can gain valuable insights and make predictions about complex real-world phenomena in an efficient and effective manner.


The quote, “With four parameters I can fit an elephant, and with five I can make him wiggle his trunk,” attributed to John von Neumann, serves as a thought-provoking reminder of the power of mathematical modeling and simplicity in explaining complex systems. It challenges us to seek elegant solutions that can accurately represent intricate phenomena with minimal complexity. Neumann’s legacy endures as an inspiration for mathematicians and scientists to push the boundaries of knowledge and discovery.

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