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The Center for Information and Bubble Studies (CIBS) studies the nature, structure, dynamics, noise and resolution of bubbles as information control problems primarily rooted in a documented feature of human nature called social proof. When uncertain as to what to believe, how to behave or act, single agents assume the beliefs, norms or actions of other agents in an attempt to reflect the correct view, stance or behavior for a given situation.

Social proof has particular manifestations in well-documented socio-informational phenomena like

  • pluralistic ignorance
  • informational cascades
  • bystander-effects
  • group-thinking
  • boom-thinking

Other lemming- behaviors herding collectives of agents in the same direction;

  • buying the same stock
  • thinking the same thing
  • holding the same opinions online
  • bullying the same people
  • shitstorming in concert
  • subscribing to the same political program
  • converging on the same enemies virtually or for real
  • all members thinking the same as the chairman of the board
  • appreciating the same art
  • “liking” the same posts on social media
  • taking the same medicine
  • subscribing to the same research program in science etc.


Irrational group behavior or wrongful belief aggregation can fuel bubbles.

Everybody is trending the same way dependent on the way in which the individual agent processes the available information about other agents’ beliefs, norms and actions but independently of whether this mode of operation is necessarily tracking the truth or is the right thing to do – irrational group behavior or wrongful belief aggregation can fuel bubbles.

The four main strands of bubble models

That bubbles over different ontologies may be viewed as information control problems in networks subject to social proof is intrinsically connected to the current information-driven models of bubble emergence in economics. The four main strands of bubble models including

(i) models in which all investors have rational expectations and symmetric information;

(ii) models for which investors are asymmetrically informed and the presence of a bubble is not common knowledge;  

(iii) models where bubbles persist due to limited arbitrage because rational and well-informed investors interact with noise traders psychologically biased in unfortunate ways and finally

(iv) models of bubbles in which different investors hold different beliefs about the fundamental value of the asset and agree to disagree accordingly.

In all four models, social information implicitly plays a key role but no over-arching information theory is yet present.

Indeed problems of informational interaction, socio-psychological influence and information flows across networks of agents or investors are acknowledged by all parties and all models of bubble formation and so "while we have a much better idea of why rational traders are unable to eradicate the mispricing introduced by behavioral traders, our understanding of behavioral biases and belief distortions is less advanced." (Brunnermeier, 2008).

Main research question

The main research question of CIBS is accordingly: May the structure and dynamics of bubbles (over different ontologies) be modelled as information control problems factoring in social proof among interacting agents together with the information-based models of bubble formation – and if so, what are the means of resolution, intervention or preemptive action?

Strategy, Methodology and Work Packages

Some of the socio-informational phenomena responsible for bubble formation are quantitative with respect to information, some qualitative. If,

  1. the generic bubble-hospitable environment over different ontologies or domains may be characterized,
  2. the underlying dynamic information structure of these phenomena may be uncovered and properly understood using formal methods and machinery and simultaneously
  3. informed, modified and corrected via feedback from simulations, experiments, empirical studies in finance, management, public spaces, social media etc., then
  4. mechanisms may be designed and policy recommendations formulated to counter (or sometimes even promote benign bubbles) the formation of bubbles.

Items (1)-(4) witness the CIBS understanding of these socio-psychological phenomena and bubble formation as information control problems to be described, analyzed and resolved in accordance with a Formal Modeling-Empirical Studies/Simulations-Mechanism Design-Triad.