Decision Making in a Low Information Environment:

Applications in Angel and Seed Investing

In December of 2000 the NASDAQ had fallen 51%. In an effort to help understand why, researchers from the Berkeley International Computer Science Institute and Rutgers authored a paper examining the effects of learning with limited information, focusing on strategic interactions “on the Internet.” However, these academic models can also help in understanding the decisions today between participants in another low information environment; angel and seed investors.

The paper examines the monopoly, synchronous and asynchronous Cournot and Stackelberg competition environments. We will consider all here (since it is difficult to label the ‘correct’ model, if there even is one). The model that is most applicable (potentially) to angel/seed investing is the asynchronous Stackelberg environment (indeed it is shown in the study that asynchronous environments tend towards Stackelberg competition. Here there is a leader (large VC’s participating in Series A, B), follower (Angel/Seed funds) structure where the leader knows ex-ante that the follower observes leader actions).

Lesson 1: Players are responsive and react quickly to changes in environment even if their current near-equilibrium payoffs are not affected

Since angel and seed investing is a low information environment, players may find it hard to attribute high payoff events with their corresponding investment actions. As a result actions that provided a high ‘equilibrium’ payoff state are potentially unknown so when they observe experimentation by other players they may perceive the other player to have more direct (higher payoff) information. The importance of building a strong negative feedback loop is examined later.

Lesson 2: Instead of independent probabilities of experimentation subjects appear to enter autocorrelated ‘experimentation phases’

This is a very surprising result and may provide some insight into how tech bubbles are formed around these ‘autocorrelated experimentation phases’. Autocorrelated because they follow a repeatable pattern in each returning occurrence of the experimentation phase.

Lesson 3: The greater variation a player can have in the payoffs of opponents the greater the inherent instability of the system where the opponent is more likely to experiment

It could be argued that venture capital follows a Stackelberg competitive environment; there is a leader and a follower and the leader knows ex ante the follower will respond to his or her actions. Here the leader is the institutional VC and the follower is the angel. The angel is incentivized to align with actions of the institutional VC since the angel’s investment is potentially dependent on future funding from a VC. In this system the variation is extreme from bankruptcy to IPO leading to, as the paper finds, greater instability and higher rates of experimentation.

Lesson 4: Players can be led to confuse experimentation by opponents with changes in underlying payoffs leading to a cascading effect of experimentation

Cascades of experimentation in the startup investing environment doesn’t sound good. But it does allow us to have greater insight into the competitive pressures leading to tech bubbles. One VC ‘experimenting’ in a new internet startup is observed by all others as a change in the underlying payoffs each could be expected to gain leading to cascading experimentation from all.

Understanding the competition in a low information environment could provide valuable insights into the creation and sustaining nature of tech bubbles. Since it is very difficult to connect high payoff events (10x investment) with the direct causes of this success (team, market, execution, tech?) players are incentivized to experiment (all the way to NASDAQ -75%.)

Structure for reviewing potential investment: Input is information on new investment (the pitch), B outcome of previous investment decisions, A is structure for filtering investments based on inputs (diligence etc.), output is investment decision

So what can we learn from this? In my view it highlights the value in building a robust negative feedback loop for investment decisions. Here we take as ‘Input’ the information on the current potential investment as well as the information on competitive experimentation (from other angels/VC’s). We also take ‘B’ the result of our previous investment decisions and feed both through ‘A’ our decision model for the current investment and arrive at ‘Output’ our investment decision (which the result will then be fed back to inform the next potential investment).

Without doubt this system is employed by a great number of successful angels and VC’s. Formalizing its structure can be beneficial so as to be balance ‘new’ competitive experimentation information with ones own investment theses and diligence structures (to avoid cascading experimentation).

Most importantly this formalized negative feedback loop allows the angel or VC to learn what decisions/theses/ideas/diligence allowed them to arrive at a successful payoff (and which didn’t). The issue here is the latency between input decision and output (payoff result) which should be the subject of another post since this one is almost TL;DR. Takeaway: Build formalized negative feedback loops for your investment decisions.

*There may be situations in the angel/seed application here that do not correlate exactly to theory (Cournot and Stackelberg competition) however this application is designed to illustrate concepts for discussion rather than add to the academic literature