A major challenge in efficiently solving distributed resource allocation problems is to cope with the dynamic state changes that characterise such systems. An effective solution to this problem should be able to detect state changes and determine why they occur (diagnosing the cause) in order to adapt to the prevailing situation. Now, since agents typically have localised views and communication constraints that prohibit global instantaneous synchronisation, we argue that cooperative information-sharing can provide them with the necessary adaptiveness and diagnostics ability. To this end, we develop a novel information-sharing algorithm for resource allocation tasks by building upon the most effective algorithm currently available in this domain. Then, using empirical analyses on a resource allocation application with dynamic state changes, network call routing with network failures, we show that, compared to the benchmark, our new algorithm achieves up to a 20% increase in call throughput, up to 3.5 times faster throughput recovery after failures, and provides a novel mechanism for distributed failure diagnosis without false positives and false negatives.