A decision tree is a useful tool in decision making that helps you choose between several options. A decision tree begins with a single root and branches out to numerous solutions, much like a tree. It is an important graphic representation of the decision-making process that allows decision-makers to perceive their thoughts in a methodical manner. It also assists the decision maker in visualizing the thought process and identifying multiple viable solutions to a particular situation. When applied in real life situations, a decision tree is complicated because it may end up with many options that exceed a single page (Zhou & Wang, 2012). One of the personal decisions that I plan to make is to use my farm for either commercial or agricultural purposes. How I will use the land depends on the availability of money to develop the land.
Availability of Funds
Yes No
Develop the land
Leave it idle
Rainy Dry Prime
Cash crops ($200,000)
Commercial & Real Estate ($190,000
Animal rearing ($140,000)
Coffee ($202,000)
Tea ($205,000)
Before developing the land, I must first ensure that I have enough funds to develop the land. In this regard, the decision that starts the decision tree is the root or the first decision point – availability of funds. The decision on whether to develop the land depends on whether or not there is enough funds to develop it. The alternatives available here are either yes or no. If there are no funds to develop the land, I will leave it idle. If there are enough funds, there will be other alternative choices to make regarding how to develop the land.
Developing the land depends on three decision points – rainy, dry and prime. If the area where the land lies experiences enough rainfall, the land can be tilled to produce cash crops. On the other hand, a dry land will be suitable for keeping drought-resistant animals such as goats and sheep. In a prime land such as cities, near transport facilities or a quarry can be used for commercial purpose to build offices, real estate, and mining. In case the land is found to be suitable for agriculture, there will be two alternatives to consider – tea or coffee. A cool and wet climate can be suitable for both tea and coffee, so it is necessary to consider other factors to choose between the two crops, including the marketability, prices, production process, and distribution.
The squares in the decision tree are the key elements of decision making (Labib, 2014). In the decision of developing land, the key elements include: availability of funds; developing the land or leaving it idle; planting cash crops, rearing animals, or building commercial houses; and planting tea or coffee. These elements are called nodes (Polan, 2011). Branches are used to connect the key decision elements until the final decision is reached.
The key decision elements can be assigned values to determine the most suitable decision to be made (Fazlollahtabar & Arabshahi, 2016). In this case, each key element is assigned monetary value to show the decision that is likely to give the highest value and benefits to the decision maker. In the personal decision of developing land, the best decision would be to plant cash crops based on its value. Assuming that the value of cash crops is at least $200,000 and the values of animal rearing and commercial development are $140,000 and $190,000 respectively. Therefore, the best outcome is cash crop farming.
After choosing cash crop farming, the decision that follows is whether to plant tea or coffee. From here, the value of each crop is used to make the best choice. The value of tea ($205,000) is higher than the value of Coffee ($202,000). For this reason, the best outcome from the decision tree is to do cash crop farming in the land, and the most cash valuable cash crop to plant is tea. According to Rajkovič (2011), the best outcome in the decision tree should have the highest value over other alternative outcomes.
In conclusion, the decision tree helps in making decisions when faced with many options. Each decision element is represented by a node and connected to other decision elements with a branch or arrows. These elements are connected until the decision maker arrives at the best outcome. Give the opportunity to choose what to do with a piece of land, a decision tree shows that with the availability of money the best option is to plant tea because it has the highest value.
References
Bleistein, S. (2017). Rapid organizational change. Hoboken: Wiley
Cha, S. and Tappert, C.C. (2009). A Genetic Algorithm for Constructing Compact Binary Decision Trees. Journal of Pattern Recognition Research, 4 (1), 1–13.
Fazlollahtabar, H., & Arabshahi, H. (2016). Iterative Dichotomiser Decision Tree for Risk Analysis in Innovation Management. International Journal of Risk and Contingency Management. 5(4), 16-26.
Labib, A. (2014). Learning from failures: Decision analysis of major disasters. Burlington: Elsevier Science.
Polan, J. (2011). Decision making. Tactical Response, 9(2), 4-6.
Rajkovič, V. (2011). Group decision making: Integrating different interests into a joint decision. Organizacija, 44(6), 175-178.
Zhou, G., & Wang, L. (2012). Co-location decision tree for enhancing decision-making of pavement maintenance and rehabilitation. Transportation Research Part C, 21(1), 287-305.
