Human beings are faced with situations that need them to make both major and minor decisions that determine the direction their lives face both in the short-term and in the long-term (Sabatucci & Cossentino, 2015). This necessitates individuals to think and evaluate their options before settling on the best option that will benefit their lives. Critical and creative skills, as well as divergent thinking, are some of the most important skills that enable a person to solve issues that face an individual in everyday life. One of the problems that I would like to solve is a relationship problem that I may encounter in the future. I might be facing issues with my partner regarding the custody of our children, justifying the need to make a decision that considers the interests and welfare of the kids. When a relationship is no longer working, the two adults are expected to make a decision on the best approach that will be used to solve the current situation, while focusing on the wellbeing of children.
The current relationship problem will be solved using three main approaches namely, solutions means-end analysis, analogy heuristic, and algorithm, which are used to arrive at solutions. The means-end analysis method is a strategy that allows an individual to find a process , or sequence that will provide good results (Sabatucci & Cossentino, 2015). The desirable goal is identified and means to arrive at it are evaluated so that results can be achieved in the least time possible. As a person moves towards the ultimate solution, progress tracking becomes important to ensure that the focus is maintained throughout the entire process (Sabatucci & Cossentino, 2015). Solving the relationship problem where intimate partners are having issues that may necessitate a divorce will need both parties to understand the differences that exist between them. After pointing out the personal differences that make it impossible for them to live together in the future, both partners need to culminate a way to relate so that they can take care of their children. The desirable goal is that their children grow up in a healthy and peaceful environment and have the love and affection of both parents. Therefore, parents can choose to have either of the parents stay with the children as the other provides for their education and health needs. For instance, the mother can stay with the kids and allow the father to meet them during weekends to spend time with them. The result for this is a separation that will lead to a divorce.
Comparatively, the analogy heuristic is a problem-solving approach that allows an individual to be inspired by past events to arrive at a solution to the current challenge (Chan, Paletz, & Schunn, 2012). Past issues and situations shape the character, perception, and behavior of individuals, meaning that people are quick to refer to get inspiration from past events that produced quality solutions to help design a strategy that will solve the current problem. However, if one has not experienced a challenge in the past that can be used as a reference for the future, a person can then opt for an alternative. The alternative should produce the desired results (Chan, Paletz, & Schunn, 2012). In the current situation where a man and his wife are to go separate ways, the analogy heuristic problem-solving approach can be used to help the partners develop a goal strategy that will provide solutions. Since the two partners have never faced this problem again in the past, they will need to choose the next alternative that will allow them to make a decision. The hill-climbing heuristic is an alternative decision-making process that the couple can use. This method is not defined as it adopts a trial and error approach. The couple will choose the best solution that appears to favor them. For instance, the couple can decide to live together by putting their differences aside for the sake of their children. This will require the couple to solve the differences they had considered irreconcilable. Constant and open communication will give them the chance to solve issues that prevent them from relating as a husband and wife. They might also need to go for marriage counseling as they initiate the reconciliation process.
An algorithm method is adopted as a problem-solving approach as it guides an individual to choose the best procedure to arrive at a solution (Cheng & Jin, 2015). It involves a procedural and defined process that allows a person to follow a step-by-step path until a solution is found. One must carefully follow the steps so that the desired result can be achieved within the shortest time and within the frameworks expected. The algorithm method is a preferred strategy where accuracy and the best yield is a preference for the decision-makers. The couple can use the algorithm approach to solve their marital problems by considering following the same procedure they used when they were getting to know each other to the extent of getting married. For instance, the couple might have gone for pre-marital counseling that prepared them for marriage life. Hence, they can revisit the pre-marital counselors to help them solve the issue at hand. The counselors may help them identify solutions that will enable them to live in peace despite their differences. The result for this is a reconciliation for the couple.
The algorithm method is easier to use compared to the other two approaches because it necessitates the adoption of a sequential process that produces accurate results (Cheng & Jin 2015). This is because the couple must have gone through some counseling processes that prepared them for the challenges ahead, and since they are unable to solve the issues as a team, a third party becomes necessary. The step-by-step approach to identify what caused the problem would allow the couple to think objectively and reconsider divorcing. Each problem-solving approach did not provide the same result because the considerations made in each alternative were different. In conclusion, each problem-solving technique is effective and provides solutions for the couple, whether they decide to part ways or stay together.
Chan, J., Paletz, S. B., & Schunn, C. D. (2012). Analogy as a strategy for supporting complex problem solving under uncertainty. Memory & cognition, 40(8), 1352-1365.
Cheng, R., & Jin, Y. (2015). A social learning particle swarm optimization algorithm for scalable optimization. Information Sciences, 291, 43-60.
Sabatucci, L., & Cossentino, M. (2015, May). From means-end analysis to proactive means-end reasoning. In Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (pp. 2-12). IEEE Press.
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