*Biomimicry, Big Data and Artificial Intelligence for a Dynamic Climate Change Management… DOI: http://dx.doi.org/10.5772/intechopen.84406*

practices that may present long-term benefits. In addition, sometimes humans struggle to gather, analyse and link varied and vast amounts of data to generate information that appropriately informs policy and practice. This condition extends to many disciplines of social and economic development. The Mjimba-Sibanda dynamic policy model seeks to avoid some of these human shortcomings. The model proposes to combine lessons from biological systems, with the new concepts of Big Data and machine learning/artificial intelligence to define critical policy components that can aid the management of climate change.

As stated earlier nature learns and adapts both in the short and long terms. Mother Nature is the biggest teacher on earth. Through biomimicry, humans learn to emulate nature both at individual and system levels. However, lessons from nature are numerous and nuanced to an extent that the human mind may not adequately decipher the relations in these lessons. Modern computer technology serves to address this shortcoming, and the rise of artificial intelligence, especially machine learning, among other related concepts, offers an opportunity for improved decision-making to improve human conditions on earth. At this point it is important to allay fears of machines taking over the human-decision function.

Our belief is that humans working with machines, each contributing what it is good at, can produce outcomes that are much better than when humans and machines working separately. Furthermore, our position is that even in democratic societies, there are policy shifts that need to, can and must avoid the bargaining vagaries of the policy cycle and shift automatically when the relevant and adequate amounts of data accurately and appropriately generates credible information to develop fair, transparent and equitable policies. This is important in an environment which data generation and analysis are happening with greater speeds, the severity and frequency of climate change-linked extreme weather events are increasing and political expediency is sometimes overriding genuine environmental concerns with long-term detrimental effects. In such a scenario, rapid and to an extent automatic policy shifts are important. What is critical is that automatic changes in one area or department should in turn trigger relevant policy changes across government departments, private, public sectors and industries to deliver a system-wide change that avoids policy incoherencies and conflicts. Where human intervention can override machine decisions, the process should be transparent to all relevant stakeholders to prevent abuse by those with the overriding capability and authority. More important is that all overrides must always leave an auditable and public trail log of who effected the changes and the corresponding rationale.

The journey towards a new policymaking approach begins!

*Changing Ecosystems and Their Services*
