The Domino Effect is a term used to describe a series of events that lead to the unexpected and often unpleasant consequences of an incident. The domino effect is also a metaphor for the way in which one event can cause an chain reaction that leads to other, unexpected events. The Domino Effect is a popular phrase that has many meanings and is often used in the context of personal and business interactions.
The earliest known use of the term “domino” was in a 1750 French play. It was also used at the time to refer to a long hooded cloak worn by a priest over a surplice, suggesting that it may have been inspired by domino pieces with contrasting black and ivory faces. The word eventually came to refer to the game as well.
Domino is a tile-based tabletop game. Traditionally, a domino set consists of 28 rectangular tiles, each bearing from one to six pips or dots on both sides. A double-six set has 56 unique combinations of ends and a domino can be arranged in lines or angular patterns to form an array.
Various games are played with dominoes, including blocking and scoring games where players try to make the most points by placing the tiles in such a way as to line up the matching ends of two or more adjacent tiles. Other types of games include solitaire and trick-taking, where the goal is to win by accumulating a number of points before the opponent does.
The earliest domino sets were made from natural materials, such as bone, silver lip ocean pearl oyster shell (mother of pearl), ivory, or dark hardwoods such as ebony. More recently, domino sets have been made from other materials such as stone (e.g., marble, granite or soapstone); metals; ceramic clay; and even frosted glass. These more modern sets have a more novel look and are usually considerably heavier than their traditional European counterparts.
There are numerous ways to model and predict the impact of a domino accident. However, modeling the domino effects involves a high degree of uncertainty. Khakzad (2015) developed a dynamic Bayesian network (DBN) model to better model the spatial and temporal escalation of domino accidents than ordinary BN models, which only consider the most probable sequence of events.
In order to improve the accuracy of this type of modeling and prediction, additional research is needed to model the impact of the uncertainty that exists in a domino accident. This includes modeling synergistic effects, parallel effects and superimposed effects.
In addition to this work, a methodology is needed to analyze and evaluate the effectiveness of existing protection strategies in mitigating the impact of a domino accident. This will help to guide future research in this area.